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Less effective information is obtained by the object detection network, due to the small size of the detection object in the entire image, the complex background, and the dense object in unmanned aerial vehicle (UAV) images. In response to the difficulties encountered, a small object detection method in UAV images is proposed as an improved YOLOv5-based algorithm in this paper. First, the space-to-depth(SPD) conv module is introduced into the basic feature extraction network, to improve significant loss of image information during downsampling. Then, various attention mechanisms are added, to intensify the acquisition of regions of interest in UAV images. Finally, the multiscale detection module is improved, to enhance the network's ability to detect small objects in UAV images. By conducting experiments on the VisDrone-DET2019 dataset, the test results of the established model show. The improved algorithm achieved a Mean Average Precision (mAP) of 41.8%, which is 7.8% better than the baseline network. In addition, the detection performance is better than most current mainstream target detection algorithms and is of some practical value. [ABSTRACT FROM AUTHOR]

The production quality of medical fluid bags is closely related to patient health. In this paper, we used medical fluid bags to detect whether they contained foreign bodies. A visual acquisition system for the fluid bag was built. Vignetting correction was performed on the acquired images, and a foreign body recognition detection method based on an improved Faster R-CNN model was proposed. The feature extraction network of Faster R-CNN was discussed and studied regarding the characteristics of small foreign objects in liquid bags, and the ResNet152 network replaced the VGG16 network; furthermore, the feature fusion and attention mechanism were added to the feature extraction, and CIoU replaced the IoU loss function; the anchor box parameters were optimized and improved using the K-means clustering algorithm, and ROI Align replaced the ROI Pooling module. The improved network in this paper was compared with the Faster R-CNN model, which is a modification of feature extraction networks, such as ResNet50, ResNet101, and ResNet152, and the original VGG16 feature extraction network. The results show that the ResNet152 network had the best feature extraction effect among the feature extraction networks, and other optimizations were performed in this paper based on the use of ResNet152. In the precision−recall curve, the network in this paper showed the best effect. The improved algorithm presented in this paper was significantly improved compared with the original algorithm, with a detection accuracy of 97% and an average accuracy improvement of 7.8% in foreign object recognition. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) aims to identify the most visually striking objects in a scene, simulating the function of the biological visual attention system. The attention mechanism in deep learning is commonly used as an enhancement strategy which enables the neural network to concentrate on the relevant parts when processing input data, effectively improving the model's learning and prediction abilities. Existing saliency object detection methods based on RGB deep learning typically treat all regions equally by using the extracted features, overlooking the fact that different regions have varying contributions to the final predictions. Based on the U2Net algorithm, this paper incorporates the split coordinate channel attention (SCCA) mechanism into the feature extraction stage. SCCA conducts spatial transformation in width and height dimensions to efficiently extract the location information of the target to be detected. While pixel-level semantic segmentation based on annotation has been successful, it assigns the same weight to each pixel which leads to poor performance in detecting the boundary of objects. In this paper, the Canny edge detection loss is incorporated into the loss calculation stage to improve the model's ability to detect object edges. Based on the DUTS and HKU-IS datasets, experiments confirm that the proposed strategies effectively enhance the model's detection performance, resulting in a 0.8% and 0.7% increase in the F1-score of U2Net. This paper also compares the traditional attention modules with the newly proposed attention, and the SCCA attention module achieves a top-three performance in prediction time, mean absolute error (MAE), F1-score, and model size on both experimental datasets. [ABSTRACT FROM AUTHOR]

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network's sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network's localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%. [ABSTRACT FROM AUTHOR]

Accurate and rapid response in complex driving scenarios is a challenging problem in autonomous driving. If a target is detected, the vehicle will not be able to react in time, resulting in fatal safety accidents. Therefore, the application of driver assistance systems requires a model that can accurately detect targets in complex scenes and respond quickly. In this paper, a lightweight feature extraction model, ShuffDet, is proposed to replace the CSPDark53 model used by YOLOX by improving the YOLOX algorithm. At the same time, an attention mechanism is introduced into the path aggregation feature pyramid network (PAFPN) to make the network focus more on important information in the network, thereby improving the accuracy of the model. This model, which combines two methods, is called ShuffYOLOX, and it can improve the accuracy of the model while keeping it lightweight. The performance of the ShuffYOLOX model on the KITTI dataset is tested in this paper, and the experimental results show that compared to the original network, the mean average precision (mAP) of the ShuffYOLOX model on the KITTI dataset reaches 92.20%. In addition, the number of parameters of the ShuffYOLOX model is reduced by 34.57%, the Gflops are reduced by 42.19%, and the FPS is increased by 65%. Therefore, the ShuffYOLOX model is very suitable for autonomous driving applications. [ABSTRACT FROM AUTHOR]

Simple Summary: The red palm weevil is an exotic and highly endangered pest that is extremely harmful to palm plants. In order to effectively control this pest, we propose an algorithm to automatically detect and count adult red palm weevils in traps. Previously, the trapping and counting of adult red palm weevils was done manually. The population density and damage level were then inferred from the number of adults trapped to guide control efforts. However, the efficiency of this method is very low. The algorithm proposed in this paper solves the drawbacks of manual counting, and the recognition accuracy reaches 93.8%, which also improves the efficiency of agricultural monitoring. The red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs' management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests. [ABSTRACT FROM AUTHOR]

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms. [ABSTRACT FROM AUTHOR]

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm's backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower. [ABSTRACT FROM AUTHOR]

3D object detection, as an essential part of perception, plays a principal role in the autonomous driving system. The cost-competitive monocular 3D object detection has drawn increasing attention recently. However, it still suffers an inferior accuracy especially for occluded objects due to the limited camera view. Inspired by compositional models, in which an object is represented as a combination of multiple components, this paper proposes a new monocular 3D object detection method that decreases the impact of occlusion by utilizing an object's front and side cues. To do this, the features are extracted from a decoupled front and side representation and then fused by an attention-based module to obtain a more consistent feature distribution. An uncertainty-guided depth ensemble based on geometry is further applied to refine the depth prediction. Experiment results demonstrate that as compared to the conventional methods, the proposed method significantly improves the detection performance for occluded objects while still satisfying real-time efficiency, with the Average Precision on 40 recall positions (AP40), respectively, increasing by 10.23% for partly occluded objects and 12.22% for mostly occluded objects in the KITTI benchmark. The codes are released at https://github.com/kagurua/Front-Side-Det [ABSTRACT FROM AUTHOR]

Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% (0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks. [ABSTRACT FROM AUTHOR]

For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CERYOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm's capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the backbone to improve the training and inference capabilities. The Efficient Intersection over Union (EIoU) loss is also used as the localization loss function, which reduces the error detection rate and missed detection rate of the algorithm. The experimental results of the CERYOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition) dataset show that the mAP(mean Average Precision) score of the algorithm is 86.1%, which is a 2.2% improvement compared to the YOLOv7. The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments, and it is more suitable for underwater target detection. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images have the characteristics of complex background environment, clustering of objects, etc., the complex background makes the remote sensing image contain a large number of irrelevant ground objects with a high similarity or overlap, which makes the edge and texture of the objects not clear enough, and this leads to low recognition accuracy of ground objects such as airports, dams, and golf field, although the size of this object is large. Based on this problem, this paper proposes a remote sensing image object detection method based on the YOLOv5 network. By improving the backbone extraction network, the network structure can be deepened to get more information about large objects, and the detection effect can be improved by adding an attention mechanism and adding an output layer to enhance feature extraction and feature fusion. The pre-training weight is obtained by transfer learning and used as the training weight of the improved YOLOv5 to speed up the network convergence. The experiment is carried out on the DIOR dataset, the results show that the improved YOLOv5 network can significantly improve the accuracy of large object recognition compared with the YOLO series network and the EfficientDet model on DIOR dataset, and the mAP of the improved YOLOv5 network is 80.5%, which is 2% higher than the original YOLOv5 network. [ABSTRACT FROM AUTHOR]

With the objective of achieving "double carbon," the power grid is placing greater importance on the security of transmission lines. The transmission line corridor has complex situations with external force targets and irregularly featured objects including smoke. For this reason, in this paper, the high-performance YOLOX-S model is selected for transmission line corridor external force object detection and improved to enhance model multi-object detection capability and irregular feature extraction capability. Firstly, to enhance the perception capability of external force objects in complex environment, we improve the feature output capability by adding the global context block after the output of the backbone. Then, we integrate convolutional block attention module into the feature fusion operation to enhance the recognition of objects with random features, among the external force targets by incorporating attention mechanism. Finally, we utilize EIoU to enhance the accuracy of object detection boxes, enabling the successful detection of external force targets in transmission line corridors. Through training and validating the model with the established external force dataset, the improved model demonstrates the capability to successfully detect external force objects and achieves favorable results in multi-class target detection. While there is improvement in the detection capability of external force objects with random features, the results indicate the need to enhance smoke recognition, particularly in further distinguishing targets between smoke and fog. [ABSTRACT FROM AUTHOR]

Small object detection has a broad application prospect in image processing of unmanned aerial vehicles, autopilot and remote sensing. However, some difficulties exactly exist in small object detection, such as aggregation, occlusion and insufficient feature extraction, resulting in a great challenge for small object detection. In this paper, we propose an improved algorithm for small object detection to address these issues. By using the spatial pyramid to extract multi-scale spatial features and by applying the multi-scale channel attention to capture the global and local semantic features, the spatial pooling pyramid and multi-scale channel attention module (SPP-MSCAM) is constructed. More importantly, the fusion of the shallower layer with higher resolution and a deeper layer with more semantic information is introduced to the neck structure for improving the sensitivity of small object features. A large number of experiments on the VisDrone2019 dataset and the NWPU VHR-10 dataset show that the proposed method significantly improves the Precision, mAP and mAP50 compared to the YOLOv5 method. Meanwhile, it still preserves a considerable real-time performance. Undoubtedly, the improved network proposed in this paper can effectively alleviate the difficulties of aggregation, occlusion and insufficient feature extraction in small object detection, which would be helpful for its potential applications in the future. [ABSTRACT FROM AUTHOR]

Aiming at the low detection accuracy and poor positioning for small objects of single-stage object detection algorithms, we improve the backbone network of SSD (Single Shot MultiBox Detector) and present an improved SSD model based on multi-scale feature fusion and attention mechanism module in this paper. Firstly, we enhance the feature extraction ability of the shallow network through the feature fusion method that is beneficial to small object recognition. Secondly, the RFB (Receptive Field block) is used to expand the object's receptive field and extract richer semantic information. After feature fusion, the attention mechanism module is added to enhance the feature information of important objects and suppress irrelevant other information. The experimental results show that our algorithm achieves 80.7% and 51.8% mAP on the PASCAL VOC 2007 classic dataset and MS COCO 2017 dataset, which are 3.2% and 10.6% higher than the original SSD algorithm. Our algorithm greatly improves the accuracy of object detection and meets the requirements of real-time. [ABSTRACT FROM AUTHOR]

The current object detection algorithm has the problem of easy loss of feature information for small object detection, which can be alleviated by using the network to process high-resolution feature map data, but it has the shortcomings of insufficient semantic information and large computational burden. To remedy these shortcomings, this paper proposes a feature extraction network that effectively handles high-resolution feature maps and parallel connections of multiple depth subnetworks. This paper constructs an input image pyramid, builds a parallel connection structure of multi-depth branch subnets, uses a shallow network to process high-resolution feature maps in the image pyramid, and uses a deep network to process low-resolution feature maps, multi-branch runs at the same time and performs two feature fusions in the middle position, fully combining high-resolution feature information and low-resolution semantic information. Using the fusion factor to build a multi-scale feature fusion structure that is highly targeted to small targets to enhance the detection ability of small targets. Using attention mechanism to further improve feature extraction ability. Experiments on the public dataset AI-TOD show that the designed feature extraction network has stronger detection ability for small targets than other commonly used feature extraction networks. Replacing the original backbone network on the two-stage classic model Faster-RCNN, the one-stage classic model SSD, YOLOv3 and the anchor-free classic model CenterNet. Compared with the original, the average detection accuracy of mAP is increased by 2.7, 3.4, 3.3 and 1.7 percentage points respectively, which proves the applicability and effectiveness of the proposed network structure. [ABSTRACT FROM AUTHOR]

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To address the issue of low accuracy in insulator object detection within power systems due to a scarcity of image sample data, this paper proposes a method for identifying insulator objects based on improved few-shot object detection through feature reweighting. The approach utilizes a meta-feature transfer model in conjunction with the improved YOLOv5 network to realize insulator recognition under conditions of few-shot. Firstly, the feature extraction module of the model incorporates an improved self-calibrated feature extraction network to extract feature information from multi-scale insulators. Secondly, the reweighting module integrates the SKNet attention mechanism to facilitate precise segmentation of the mask. Finally, the multi-stage non-maximum suppression algorithm is designed in the prediction layer, and the penalty function about confidence is set. The results of multiple prediction boxes are retained to reduce the occurrence of false detection and missing detection. For the poor detection results due to a low diversity of sample space, the transfer learning strategy is applied in the training to transfer the entire trained model to the detection of insulator targets. The experimental results show that the insulator detection mAP reaches 29.6%, 36.0%, and 48.3% at 5-shot, 10-shot, and 30-shot settings, respectively. These findings serve as evidence of improved accuracy levels of the insulator image detection under the condition of few shots. Furthermore, the proposed method enables the recognition of insulators under challenging conditions such as defects, occlusion, and other special circumstances. [ABSTRACT FROM AUTHOR]

With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network's attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images. [ABSTRACT FROM AUTHOR]

The limited computing resources on edge devices such as Unmanned Aerial Vehicles (UAVs) mean that lightweight object detection algorithms based on convolution neural networks require significant development. However, lightweight models are challenged by small targets with few available features. In this paper, we propose an LC-YOLO model that uses detailed information about small targets in each layer to improve detection performance. The model is improved from the one-stage detector, and contains two optimization modules: Laplace Bottleneck (LB) and Cross-Layer Attention Upsampling (CLAU). The LB module is proposed to enhance shallow features by integrating prior information into the convolutional neural network and maximizing knowledge sharing within the network. CLAU is designed for the pixel-level fusion of deep features and shallow features. Under the combined action of these two modules, the LC-YOLO model achieves better detection performance on the small object detection task. The LC-YOLO model with a parameter quantity of 7.30M achieves an mAP of 94.96% on the remote sensing dataset UCAS-AOD, surpassing the YOLOv5l model with a parameter quantity of 46.61M. The tiny version of LC-YOLO with 1.83M parameters achieves 94.17% mAP, which is close to YOLOv5l. Therefore, the LC-YOLO model can replace many heavyweight networks to complete the small target high-precision detection task under limited computing resources, as in the case of mobile edge-end chips such as UAV onboard chips. [ABSTRACT FROM AUTHOR]

Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective “U-Net in U-Net” framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at https://github.com/danfenghong/IEEE [ABSTRACT FROM AUTHOR]

With the gradual increase in the annual production of citrus, the efficiency of human labor has become the bottleneck limiting production. To achieve an unmanned citrus picking technology, the detection accuracy, prediction speed, and lightweight deployment of the model are important issues. Traditional object detection methods often fail to achieve balanced effects in all aspects. Therefore, an improved YOLOv7 network model is proposed, which introduces a small object detection layer, lightweight convolution, and a CBAM (Convolutional Block Attention Module) attention mechanism to achieve multi-scale feature extraction and fusion and reduce the number of parameters of the model. The performance of the model was tested on the test set of citrus fruit. The average accuracy (mAP@0.5) reached 97.29%, the average prediction time was 69.38 ms, and the number of parameters and computation costs were reduced by 11.21 M and 28.71 G compared with the original YOLOv7. At the same time, the Citrus-YOLOv7 model's results show that it performs better compared with the current state-of-the-art network models. Therefore, the proposed Citrus-YOLOv7 model can contribute to solving the problem of citrus detection. [ABSTRACT FROM AUTHOR]

In order to alleviate the situation that small objects are prone to missed detection and false detection in natural scenes, this paper proposed a small object detection algorithm for adaptive feature fusion, referred to as MMF-YOLO. First, aiming at the problem that small object pixels are easy to lose, a multi-branch cross-scale feature fusion module with fusion factor was proposed, where each fusion path has an adaptive fusion factor, which can allow the network to independently adjust the importance of features according to the learned weights. Then, aiming at the problem that small objects are similar to background information and small objects overlap in complex scenes, the M-CBAM attention mechanism was proposed, which was added to the feature reinforcement extraction module to reduce feature redundancy. Finally, in light of the problem of small object size and large size span, the size of the object detection head was modified to adapt to the small object size. Experiments on the VisDrone2019 dataset showed that the mAP of the proposed algorithm could reach 42.23%, and the parameter quantity was only 29.33 MB, which is 9.13% ± 0.07% higher than the benchmark network mAP, and the network model was reduced by 5.22 MB. [ABSTRACT FROM AUTHOR]

Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research. [ABSTRACT FROM AUTHOR]

Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications. [ABSTRACT FROM AUTHOR]

Traffic-related multimedia analysis has become increasingly important in both research community and industry. In this paper, we study the problem of image-based classification of abnormal traffic objects. Different from previous works that focusing on only the normal object categories, our work aims to classify both the category and the working status of a traffic object. We construct a new dataset, namely Abnormal Traffic Object Classification (ATOC), for the study of the above problem. ATOC contains 6 kinds of traffic objects and for each main category there are also two sub-categories covering the normal and abnormal status of the objects. We propose a novel deep-learning based framework to solve our problem and provide a strong baseline for future studies. Specifically, we adopt a pre-trained deep convolutional network for feature extraction and use support vector machine for classification. We also utilize random sample pairing to augment the dataset and introduce attention mechanism to further refine the feature representation. Experimental results demonstrate that the proposed method achieves superior performance than the state-of-art deep learning approaches for the recognition of objects' categories and the corresponding working status in traffic scenarios. [ABSTRACT FROM AUTHOR]

Since small objects occupy less pixels in the image and are difficult to recognize. Small object detection has always been a research difficulty in the field of computer vision. Aiming at the problems of low sensitivity and poor detection performance of YOLOv3 for small objects. AFYOLO, which is more sensitive to small objects detection was proposed in this paper. Firstly, the DenseNet module is introduced into the low-level layers of backbone to enhance the transmission ability of objects information. At the same time, a new mechanism combining channel attention and spatial attention is introduced to improve the feature extraction ability of the backbone. Secondly, a new feature pyramid network (FPN) is proposed to better obtain the features of small objects. Finally, ablation studies on ImageNet classification task and MS-COCO object detection task verify the effectiveness of the proposed attention module and FPN. The results on Wider Face datasets show that the AP of the proposed method is 11.89%higher than that of YOLOv3 and 8.59%higher than that of YOLOv4. All of results show that AFYOLO has better ability for small object detection. [ABSTRACT FROM AUTHOR]

At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in X-direction and 1.73 mm in Y-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SEResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M. [ABSTRACT FROM AUTHOR]

A lightweight network target detection algorithm was proposed, based on MobileNetv2_CA, focusing on the problem of high complexity, a large number of parameters, and the missed detection of small targets in the target detection network based on candidate regions and regression methods in autonomous driving scenarios. First, Mosaic image enhancement technology is used in the data pre-processing stage to enhance the feature extraction of small target scenes and complex scenes; second, the Coordinate Attention (CA) mechanism is embedded into the Mobilenetv2 backbone feature extraction network, combined with the PANet and Yolo detection heads for multi-scale feature fusion; finally, a Lightweight Object Detection Network is built. The experimental test results show that the designed network obtained the highest average detection accuracy of 81.43% on the Voc2007 + 2012 dataset, and obtained the highest average detection accuracy of 85.07% and a detection speed of 31.84 FPS on the KITTI dataset. The total amount of network parameters is only 39.5 M. This is beneficial to the engineering application of MobileNetv2 network in automatic driving. [ABSTRACT FROM AUTHOR]

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator. [ABSTRACT FROM AUTHOR]

In this paper, we propose a 3D object detection method called MMAF-Net that is based on the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data. This is an end-to-end architecture, which combines the characteristics of RGB images, the front view of point clouds based on reflection intensity, and the bird's eye view of point clouds. It also adopts a multi-stage fusion approach of "data-level fusion + feature-level fusion" to fully exploit the strength of multimodal information. Our proposed method addresses key challenges found in current 3D object detection methods for autonomous driving, including insufficient feature extraction from multimodal data, rudimentary fusion techniques, and sensitivity to distance and occlusion. To ensure the comprehensive integration of multimodal information, we present a series of targeted fusion methods. Firstly, we propose a novel input form that encodes dense point cloud reflectivity information into the image to enhance its representational power. Secondly, we design the Region Attention Adaptive Fusion module utilizing an attention mechanism to guide the network in adaptively adjusting the importance of different features. Finally, we extend the 2D DIOU (Distance Intersection over Union) loss function to 3D and develop a joint regression loss based on 3D_DIOU and SmoothL1 to optimize the similarity between detected and ground truth boxes. The experimental results on the KITTI dataset demonstrate that MMAF-Net effectively addresses the challenges posed by highly obscured or crowded scenes while maintaining real-time performance and improving the detection accuracy of smaller and more difficult objects that are occluded at far distances. • A novel object detection method is proposed integrating RGB images and LiDAR data. • This study tests the method on the KITTI dataset. • This method tackles highly obscured or crowded scenes for autonomous driving. • This method improves the detection accuracy of smaller and occluded targets far away. [ABSTRACT FROM AUTHOR]

Remote sensing scene understanding is crucial for extracting valuable information from high-resolution images, including object detection and classification. Traditional object detection methods face challenges in handling the diverse scales, orientations, and complex backgrounds present in remote sensing data. In this paper, we propose a novel remote sensing scene understanding system called multiscale-attention R-CNN (MSA R-CNN), which incorporates a super multiscale feature extraction network (SMENet) for enhanced feature extraction from multiscale images, an adaptive dynamic inner lateral (ADIL) connection module to tackle information loss in feature pyramid networks (FPN), and a distributed lightweight attention module (DLAM) to refine feature information processing. Furthermore, a new dataset combining the DIOR and DOTA datasets is introduced to extract the background information of detected objects and evaluate the proposed system's performance. MSA R-CNN achieved an mAP of 74.37% on the DIOR dataset when the gamma value was set to 0.2 and 81.97% on the DOTA dataset when the gamma value was set to 0.1 with the same learning rate, outperforming state-of-the-art models on both datasets. The proposed system demonstrates significant improvements in both object detection and background information extraction, providing a comprehensive solution for remote sensing scene understanding. • MSA R-CNN is proposed for remote sensing scene understanding. • SMENet enhances multi-scale feature extraction in MSA R-CNN. • ADIL module tackles FPN information loss. • DLAM refines feature information processing in FPN. • Achieved state-of-the-art performance on DIOR & DOTA dataset. [ABSTRACT FROM AUTHOR]

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network's attention to the details of small targets and improve the model's ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively. [ABSTRACT FROM AUTHOR]

Insufficient feature extraction of the backbone network and lack of semantic information in the shallow convolution layer often lead to poor detection results on small objects. In order to improve the accuracy and robustness of small object detection, this paper proposes a deep small object detection algorithm that integrates attention mechanism. Firstly, to address the problem of insufficient feature extraction capability of the backbone network, Darknet-53 is selected as the network of feature extraction, and a new grouped residual connection is proposed to replace the residual connection structure in the original Darknet-53. This forms a new enhanced backbone network named I-Darknet53. This grouped residual structure can effectively increase the size of the receptive field by interweaving the feature information of different channels. Secondly, in the multi-scale detection phase, a shallow feature enhancement network is proposed to obtain shallow enhanced features by fusing the shallow layer and deep layer. The network including feature enhancement module and an efficient feature fusion strategy guided by channel attention mechanism is used to improve the lack of semantic information of shallow features. Experimental results show that the proposed algorithm has better performance than the SSD algorithm on PASCAL VOC dataset. When the input image size is 300 x 300, the average accuracy of the proposed model is 80.2%; when the input image size is 500 x 500, the average accuracy of the proposed model is 82.3%. In addition, it can effectively improve the detection accuracy of small objects under the premise of ensuring the detection speed. [ABSTRACT FROM AUTHOR]

With the rapid advancements in computer vision object detection networks, several detection networks based on remote sensing data have been proposed for oriented object detection. However, existing oriented networks have limitations in terms of feature extraction and oriented information extraction. To address these limitations, in this paper, we proposed an advanced adaptive points-based anchor-free oriented detection network called RA 2 DC-Net which includes a residual augment-convolutions (Res-AugConvs) module that uses residual structures and attention mechanisms to enhance feature focus information and minimize the loss of focus feature information. In addition, we proposed a method called adaptive deformable convolution (ADConv) to fine-tune custom convolution weights according to the features and generate high-quality adaptive offsets for accurate orientation information. Furthermore, we evaluated the performance of RA 2 DC-Net on two large-scale datasets: the Dataset for Object deTection in Aerial images (DOTA), the rotated object DetectIon in Optical Remote sensing images (DIOR-R) and the High-Resolution Ship Collection 2016 (HRSC2016). Experimental results demonstrated that RA 2 DC-Net achieved mAP values of 76.25%, 68.73% and 89.75% on the three datasets, respectively, with high AP values for various object classes on DOTA. • Decreased the continuous convolutions losses of oriented information in oriented object detection. • Enabling adaptive orientations of object features improves the precision of offset calculations. • Devising a new head structure facilitates focused oriented object features. [ABSTRACT FROM AUTHOR]

When the picking robot picks green peaches, there are problems such as the color of the fruit being similar to the background color, overlapping fruits, and small fruit size, uneven lighting, and branches and leaves occlusion. As a result, the picking robot cannot quickly detect green peaches. In order to solve the above problems, a lightweight object detection network for fast detection of green peaches is proposed, which is composed of a backbone network, feature enhancement network, Lightweight Self-Attention (LSA) network, and four-scale prediction network. First, the lightweight detection unit LeanNet of the backbone network is designed, which uses the idea of deep separable convolution to achieve fast detection. Secondly, the feature enhancement module (P-Enhance) is designed, which uses convolution kernels of different receptive fields to extract different perceptual information in the feature map, which enhances the network's feature extraction ability for green peach. Then, the LSA module is designed to generate a local saliency map based on green peach features, which effectively suppressed the irrelevant area of the branch and leaf background. Finally, a four-scale prediction network is designed, in which the Four-scale Pyramid Fusion (FSPF) module can generate a four-scale feature pyramid, which includes the color and shape of the green peach at different network depths, and is conducive to the detection of small volume green peaches. The experimental results show that precision, recall, and F1 of our method in the green peach test set reached 97.3%, 99.7%, and 98.5%, respectively. In the actual picking scenes, Qualcomm Snapdragon 865 embedded devices equipped with different state-of-the-art methods are used. Through comparative experiments in various scenarios, compared with the state-of-the-art method, both in terms of experimental data and visual effects, there is a significant improvement, which can meet the real-time object detection needs of picking robots. [ABSTRACT FROM AUTHOR]

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The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has beenwidely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm's efficacy in recognizing low-resolution and small-size objects. The algorithm's feature extraction network is improved by using a Large Selective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally, the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate faster convergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves a detection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which is a significant enhancement compared to the unimproved algorithm. This improvement effectively enhances the accuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm. [ABSTRACT FROM AUTHOR]

Rising labor costs and a workforce shortage have impeded the development and economic benefits of the global grape industry. Research and development of intelligent grape harvesting technologies is desperately needed. Therefore, rapid and accurate identification of grapes is crucial for intelligent grape harvesting. However, object detection algorithms encounter multiple challenges in unstructured vineyards, such as similar background colors, light obstruction from greenhouses and leaves, and fruit occlusion. All of these factors contribute to the difficulty of correctly identifying grapes. The GrapeDetectNet (GDN), based on the YOLO (You Only Look Once) v5s, is proposed to improve grape detection accuracy and recall in unstructured vineyards. dual-channel feature extraction attention (DCFE) is a new attention structure introduced in GDN. We also use dynamic snake convolution (DS-Conv) in the backbone network. We collected an independent dataset of 1280 images after a strict selection process to evaluate GDN's performance. The dataset encompasses examples of Shine Muscat and unripe Kyoho grapes, covering a range of complex outdoor situations. The results of the experiment demonstrate that GDN performed outstandingly on this dataset. Compared to YOLOv5s, this model increased metrics such as 2.02% of mAP0.5:0.95, 2.5% of mAP0.5, 1.4% of precision, 1.6% of recall, and 1.5% of F1 score. Finally, we test the method on a grape-picking robot, and the results show that our algorithm works remarkably well in harvesting experiments. The results indicate that the GDN grape detection model in this study exhibits high detection accuracy. It is proficient in identifying grapes and demonstrates good robustness in unstructured vineyards, providing a valuable empirical reference for the practical application of intelligent grape harvesting technology. [ABSTRACT FROM AUTHOR]

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

Remote sensing image object detection is a challenging task in the field of computer vision due to the complex backgrounds and diverse arrangements of targets in remote sensing images, forming intricate scenes. To overcome this challenge, existing object detection models adopt rotated target detection methods. However, these methods often lead to a loss of semantic information during feature extraction, specifically regarding the insufficient consideration of element correlations. To solve this problem, this research introduces a novel attention module (EuPea) designed to effectively capture inter-elemental information in feature maps and generate more powerful feature maps for use in neural networks. In the EuPea attention mechanism, we integrate distance information and Pearson correlation coefficient information between elements in the feature map. Experimental results show that using either type of information individually can improve network performance, but their combination has a stronger effect, producing an attention-weighted feature map. This improvement effectively enhances the object detection performance of the model, enabling it to better comprehend information in remote sensing images. Concurrently, this also improves missed detections and false alarms in object detection. Experimental results obtained on the DOTA, NWPU VHR-10, and DIOR datasets indicate that, compared with baseline RCNN models, our approach achieves respective improvements of 1.0%, 2.4%, and 1.8% in mean average precision (mAP). [ABSTRACT FROM AUTHOR]

Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios. [ABSTRACT FROM AUTHOR]

Pine Wilt Disease (PWD) is a devastating global forest disease that spreads rapidly and causes severe ecological and economic losses. Drone remote sensing imaging technology is an effective way to detect PWD and control its spread. However, the existing algorithms for detecting PWD using drone images have low recognition accuracy, difficult image calibration, and slow detection speed. We propose a fast detection algorithm for PWD based on an improved YOLOv8 model. The model first adds a small object detection layer to the Neck module in the YOLOv8 base framework to improve the detection performance of small diseased pine trees and then inserts three attention mechanism modules on the backbone network to extend the sensory field of the network to enhance the extraction of image features of deep diseased pine trees. To evaluate the proposed algorithm framework, we collected and created a dataset in Weihai City, China, containing PWD middle-stage and late-stage infected tree samples. The experimental results show that the improved YOLOv8s-GAM model achieves 81%, 67.2%, and 76.4% optimal detection performance on mAP50, mAP50-95, and Mean evaluation metrics, which is 4.5%, 4.5%, and 2.7% higher than the original YOLOv8s model. Our proposed improved YOLOv8 model basically meets the needs of large-scale PWD epidemic detection and can provide strong technical support for forest protection personnel. [ABSTRACT FROM AUTHOR]

The detection algorithm of the apple-picking robot contains a complex network structure and huge parameter volume, which seriously limits the inference speed. To enable automatic apple picking in complex unstructured environments based on embedded platforms, we propose a lightweight YOLOv5-CS model for apple detection based on YOLOv5n. Firstly, we introduced the lightweight C3-light module to replace C3 to enhance the extraction of spatial features and boots the running speed. Then, we incorporated SimAM, a parameter-free attention module, into the neck layer to improve the model's accuracy. The results showed that the size and inference speed of YOLOv5-CS were 6.25 MB and 0.014 s, which were 4 5 and 1.2 times that of the YOLOv5n model, respectively. The number of floating-point operations (FLOPs) were reduced by 15.56%, and the average precision (AP) reached 99.1%. Finally, we conducted extensive experiments, and the results showed that the YOLOv5-CS outperformed mainstream networks in terms of AP, speed, and model size. Thus, our real-time YOLOv5-CS model detects apples in complex orchard environments efficiently and provides technical support for visual recognition systems for intelligent apple-picking devices. [ABSTRACT FROM AUTHOR]

• For most deep learning-based image fusion methods, it is still hard to solve the details degeneration problem due to the degradation of the intermediate features in the network. To overcome this difficulty, we propose a symmetrical skip attention network which is an end-to-end network for image fusion. And in our network, residual dense blocks, as well as skip connections with channel attention are used to obtain a fused image with rich details. • To minimize information loss during the training process and thus obtain the ideal fusion result, we designed a weight block to calculate the loss weight, and designed a U-net with self-attention in the extracted weight features part, which enables deep weight feature maps to retain more features. • Experiments show that our method has advantages in both subjective and objective evaluation with other image fusion methods. The performance comparison of various fusion algorithms in object detection reveals the advantages of our framework in facilitating advanced vision tasks. Infrared and visible image fusion aims to generate the desired fusion image by fusing complementary images from different sensors. Artificially generated images are more appropriate for human visual perception or further image-processing tasks. Although a variety of infrared and visible image fusion methods have been proposed in recent years, the degradation of the intermediate features and the loss of details in the network are still difficult to solve, resulting in the loss of details and generation of artifacts in the fused images. In this paper, a symmetrical skip attention network is constructed to solve these problems. The skip attention mechanism used in our network can compensate for the information lost in the feature extraction stage, which can reduce the loss of details in the fused images effectually. Meanwhile, we designed a weight block to calculate the information weight in the loss function. Thus, the network can retain source image information adaptively. A U-net with self-attention is designed to achieve the feature extraction in the weight block. The self-attention helps the network to extract more detailed features. And we conducted ablation experiments to verify the performance of different modules in the network. Extensive experimental results prove that the proposed fusion RDCa-Net is superior to the latest fusion methods in subjective and objective evaluation. In addition, we apply the fused images generated by our method to object detection. Compared with other fusion algorithms, our method has a higher confidence level for both infrared and visible targets. It proves the potential of our method in promoting advanced visual tasks. [ABSTRACT FROM AUTHOR]

To address the problem of accurate recognition and localization of multiple clusters of green persimmons with similar color to the background under natural environment, this study proposes a multi-cluster green persimmon identification method based on improved Faster RCNN was proposed by using the self-built green persimmon dataset. The feature extractor DetNet is used as the backbone feature extraction network, and the model detection attention is focused on the target object itself by adding the weighted ECA channel attention mechanism to the three effective feature layers in the backbone, and the detection accuracy of the algorithm is improved. By maximizing the pooling of the lower layer features with the added attention mechanism, the high and low dimensions and magnitudes are made the same. The processed feature layers are combined with multi-scale features using a serial layer-hopping connection structure to enhance the robustness of feature information, effectively copes with the problem of target detection of objects with obscured near scenery in complex environments and accelerates the detection speed through feature complementarity between different feature layers. In this study, the K-means clustering algorithm is used to group and anchor the bounding boxes so that they converge to the actual bounding boxes, The average mean accuracy (mAP) of the improved Faster RCNN model reaches 98.4%, which was 11.8% higher than that of traditional Faster RCNN model, which also increases the accuracy of object detection during regression prediction. and the average detection time of a single image is improved by 0.54s. The algorithm is significantly improved in terms of accuracy and speed, which provides a basis for green fruit growth state monitoring and intelligent yield estimation in real scenarios. [ABSTRACT FROM AUTHOR]

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the A P s values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) image detection algorithms are critical in performing military countermeasures and disaster search and rescue. The state-of-the-art object detection algorithm known as you only look once (YOLO) is widely used for detecting UAV images. However, it faces challenges such as high floating-point operations (FLOPs), redundant parameters, slow inference speed, and poor performance in detecting small objects. To address the above issues, an improved, lightweight, real-time detection algorithm was proposed based on the edge computing platform for UAV images. In the presented method, MobileNetV3 was used as the YOLOv5 backbone network to reduce the numbers of parameters and FLOPs. To enhance the feature extraction ability of MobileNetV3, the efficient channel attention (ECA) attention mechanism was introduced into MobileNetV3. Furthermore, in order to improve the detection ability for small objects, an extra prediction head was introduced into the neck structure, and two kinds of neck structures with different parameter scales were designed to meet the requirements of different embedded devices. Finally, the FocalEIoU loss function was introduced into YOLOv5 to accelerate bounding box regression and improve the localization accuracy of the algorithm. To validate the performance of the proposed improved algorithm, we compared our algorithm with other algorithms in the VisDrone-Det2021 dataset. The results showed that compared with YOLOv5s, MELF-YOLOv5-S achieved a 51.4% reduction in the number of parameters and a 38.6% decrease in the number of FLOPs. MELF-YOLOv5-L had 87.4% and 47.4% fewer parameters and FLOPs, respectively, and achieved higher detection accuracy than YOLOv5l. [ABSTRACT FROM AUTHOR]

As one of the most important defects of concrete, cracks seriously threaten the service life and safety of concrete structures, and various safety incidents caused by the collapse of concrete structures have occurred. Therefore, it is essential to detect concrete cracks as soon as possible. Existing object detection methods have low detection accuracy for cracks, leading to unsatisfactory detection results. In this paper, we propose a variety of feasible modules that improve the accuracy of single shot multibox detection (SSD), which is the most efficient object detection method in terms of both accuracy and speed. First, to improve the neural network's ability to learn high-level and low-level feature maps, we propose a feature fusion enhancement module (FFEM). Second, to more effectively capture the information between feature map channels, we propose convolutional network attention (CNA). Third, to improve the anchor box fit to the ground truth box, we reset the distribution of the anchor box. Last, we propose a new type of nonmaximum suppression (NMS) named T-Soft NMS to address numerous issues with current NMS and to significantly enhance the performance of the model. We tested our method on a crack dataset, and numerous tests showed that it outperformed competing methods. In addition, we carried out ablation studies to confirm the validity and efficacy of our method. • Increases the object detection algorithm's accuracy in detecting concrete cracks. • The model maintains excellent robustness even in the presence of shadow. • Feature fusion enhancement module and convolutional network attention are presented to boost the object detection model's feature extraction capacity. • Improved the existing non-maximum suppression in the post processing part of object detection. [ABSTRACT FROM AUTHOR]