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Early detection of lung nodules is essential for lung cancer screening and improving patient survival rates. Traditional object detection networks such as YOLO and Faster R-CNN have shown promising results in detecting lung nodules but often lack sufficient integration of extracted features to enhance accuracy and efficiency. Moreover, these methods typically do not retain the spatial information of lung nodules from the original CT images. To overcome these limitations, a novel lung nodule detection algorithm based on YOLOv7 is introduced. Firstly, to better preserve essential features and minimize interference from irrelevant background noise, a deformable attention module for feature fusion has been designed. Additionally, maximum intensity projection is employed to create projection images at various intensities, thereby enriching the spatial background information that is often missing in single CT slices. Thirdly, the WIoU loss function is utilized to replace the original YOLOv7 loss function, aiming to reduce the influence of low-quality samples on the gradient within the dataset. The proposed model was validated using the publicly available LUNA16 dataset and achieved a recall rate of 94.40% and an AP value of 95.39%. These results demonstrate the enhanced precision and efficiency of lung nodule detection. [ABSTRACT FROM AUTHOR]

Accurate fruit detection is crucial for automated fruit picking. However, real-world scenarios, influenced by complex environmental factors such as illumination variations, occlusion, and overlap, pose significant challenges to accurate fruit detection. These challenges subsequently impact the commercialization of fruit harvesting robots. A tomato detection model named YOLO-SwinTF, based on YOLOv7, is proposed to address these challenges. Integrating Swin Transformer (ST) blocks into the backbone network enables the model to capture global information by modeling long-range visual dependencies. Trident Pyramid Networks (TPN) are introduced to overcome the limitations of PANet's focus on communication-based processing. TPN incorporates multiple self-processing (SP) modules within existing top-down and bottom-up architectures, allowing feature maps to generate new findings for communication. In addition, Focaler-IoU is introduced to reconstruct the original intersection-over-union (IoU) loss to allow the loss function to adjust its focus based on the distribution of difficult and easy samples. The proposed model is evaluated on a tomato dataset, and the experimental results demonstrated that the proposed model's detection recall, precision, F1 score, and AP reach 96.27%, 96.17%, 96.22%, and 98.67%, respectively. These represent improvements of 1.64%, 0.92%, 1.28%, and 0.88% compared to the original YOLOv7 model. When compared to other state-of-the-art detection methods, this approach achieves superior performance in terms of accuracy while maintaining comparable detection speed. In addition, the proposed model exhibits strong robustness under various lighting and occlusion conditions, demonstrating its significant potential in tomato detection. [ABSTRACT FROM AUTHOR]

Objective: Pedestrian detection is very important for the environment perception and safety action of intelligent robots and autonomous driving, and is the key to ensuring the safe action of intelligent robots and auto assisted driving. Methods: In response to the characteristics of pedestrian objects occupying a small image area, diverse poses, complex scenes and severe occlusion, this paper proposes an improved pedestrian object detection method based on the YOLOv7 model, which adopts the Convolutional Block Attention Module (CBAM) attention mechanism and Deformable ConvNets v2 (DCNv2) in the two Efficient Layer Aggregation Network (ELAN) modules of the backbone feature extraction network. In addition, the detection head is replaced with a Dynamic Head (DyHead) detector head with an attention mechanism; unnecessary background information around the pedestrian object is also effectively excluded, making the model learn more concentrated feature representations. Results: Compared with the original model, the log-average miss rate of the improved YOLOv7 model is significantly reduced in both the Citypersons dataset and the INRIA dataset. Conclusions: The improved YOLOv7 model proposed in this paper achieved good performance improvement in different pedestrian detection problems. The research in this paper has important reference significance for pedestrian detection in complex scenes such as small, occluded and overlapping objects. [ABSTRACT FROM AUTHOR]

Effective weed management is a significant challenge in agronomic crops which necessitates innovative solutions to reduce negative environmental impacts and minimize crop damage. Traditional methods often rely on indiscriminate herbicide application, which lacks precision and sustainability. To address this critical need, this study demonstrated an AI-enabled robotic system, Weeding robot, designed for targeted weed management. Palmer amaranth (Amaranthus palmeri S. Watson) was selected as it is the most troublesome weed in Nebraska. We developed the full stack (vision, hardware, software, robotic platform, and AI model) for precision spraying using YOLOv7, a state-of-the-art object detection deep learning technique. The Weeding robot achieved an average of 60.4% precision and 62% recall in real-time weed identification and spot spraying with the developed gantry-based sprayer system. The Weeding robot successfully identified Palmer amaranth across diverse growth stages in controlled outdoor conditions. This study demonstrates the potential of AI-enabled robotic systems for targeted weed management, offering a more precise and sustainable alternative to traditional herbicide application methods. [ABSTRACT FROM AUTHOR]

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision (mAP@0.5) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks. [ABSTRACT FROM AUTHOR]

Accurate fruit detection is crucial for automated fruit picking. However, real-world scenarios, influenced by complex environmental factors such as illumination variations, occlusion, and overlap, pose significant challenges to accurate fruit detection. These challenges subsequently impact the commercialization of fruit harvesting robots. A tomato detection model named YOLO-SwinTF, based on YOLOv7, is proposed to address these challenges. Integrating Swin Transformer (ST) blocks into the backbone network enables the model to capture global information by modeling long-range visual dependencies. Trident Pyramid Networks (TPN) are introduced to overcome the limitations of PANet's focus on communication-based processing. TPN incorporates multiple self-processing (SP) modules within existing top-down and bottom-up architectures, allowing feature maps to generate new findings for communication. In addition, Focaler-IoU is introduced to reconstruct the original intersection-over-union (IoU) loss to allow the loss function to adjust its focus based on the distribution of difficult and easy samples. The proposed model is evaluated on a tomato dataset, and the experimental results demonstrated that the proposed model's detection recall, precision, F1 score, and AP reach 96.27%, 96.17%, 96.22%, and 98.67%, respectively. These represent improvements of 1.64%, 0.92%, 1.28%, and 0.88% compared to the original YOLOv7 model. When compared to other state-of-the-art detection methods, this approach achieves superior performance in terms of accuracy while maintaining comparable detection speed. In addition, the proposed model exhibits strong robustness under various lighting and occlusion conditions, demonstrating its significant potential in tomato detection. [ABSTRACT FROM AUTHOR]

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production. [ABSTRACT FROM AUTHOR]

The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition remains a challenge for existing models. We constructed a representative agricultural pest and disease dataset, FIP6Set, through a combination of field photography and web scraping. This dataset encapsulates key issues encountered in existing agricultural pest and disease datasets. Referencing existing bounding box regression (BBR) loss functions, we reconsidered their geometric features and proposed a novel bounding box similarity comparison metric, DDRIoU, suited to the characteristics of agricultural pest and disease datasets. By integrating the focal loss concept with the DDRIoU loss, we derived a new loss function, namely Focal-DDRIoU loss. Furthermore, we modified the network structure of YOLOV7 by embedding the MobileViTv3 module. Consequently, we introduced a model specifically designed for agricultural pest and disease detection in precision agriculture. We conducted performance evaluations on the FIP6Set dataset using mAP75 as the evaluation metric. Experimental results demonstrate that the Focal-DDRIoU loss achieves improvements of 1.12%, 1.24%, 1.04%, and 1.50% compared to the GIoU, DIoU, CIoU, and EIoU losses, respectively. When employing the GIoU, DIoU, CIoU, EIoU, and Focal-DDRIoU loss functions, the adjusted network structure showed enhancements of 0.68%, 0.68%, 0.78%, 0.60%, and 0.56%, respectively, compared to the original YOLOv7. Furthermore, the proposed model outperformed the mainstream YOLOv7 and YOLOv5 models by 1.86% and 1.60%, respectively. The superior performance of the proposed model in detecting agricultural pests and diseases directly contributes to reducing pesticide misuse, preventing large-scale pest and disease outbreaks, and ultimately enhancing crop yields. These outcomes strongly support the promotion of sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The civil aviation transportation has sustained rapid growth, which poses significant challenges in ensuring airport safety and efficiency of use. Persons and vehicles are tiny targets in airport surveillance. Existing detection methods are difficult to detect accurately. Enhancing small target detection by only the method of adding enhancement modules inevitably leads to increased network parameters. To address the above issues, this article proposes a lightweight airport surveillance detection based on YOLOv7 named LAS-YOLO. Firstly, we design the lightweight basic module, which significantly reduces network parameters while retaining certain local features. Secondly, we replace the SPPCSPC module with the spatial pyramid pooling-fast module with fewer parameters, further reducing the quantity of network parameters. Finally, the attention mechanism and small object detection layer are introduced to enhance small object detection accuracy. The efficient channel attention module is selected among three attention methods by experiments. We simulate the application process of object detection methods in airport surveillance, training on high-performance computers and testing on lower-performance computer. This article verifies the performance of the proposed method on the public ASS dataset consisting of the airport surface surveillance dataset (ASS1) and panoramic surveillance dataset (ASS2). The experiment shows that the parameters of LAS-YOLO are 12.5 M, which is 34.2 % of the original model. The mean average precision is 89.8 % on the ASS1. This proposed method enhances the average precision for airplane and vehicle detection by 14.5 % and 22.7 % compared to YOLOv7 on the ASS2. In order to reflect the robustness of the model in airport surveillance, we conduct another experiment using airplane data from the ROSD. The experiment demonstrates the superiority of the proposed method over other models in airport surveillance. Code is available at https://zenodo.org/records/10969930. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters. [ABSTRACT FROM AUTHOR]

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed. [ABSTRACT FROM AUTHOR]

The technology for object detection in remote sensing images finds extensive applications in production and people's lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network's semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network's overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular. [ABSTRACT FROM AUTHOR]

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation ( R 2 = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation. [ABSTRACT FROM AUTHOR]

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model's ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. [ABSTRACT FROM AUTHOR]

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Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements. [ABSTRACT FROM AUTHOR]

Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with mainstream algorithms. Experimental results demonstrate a 70.2% reduction in network parameters and a 6.3% improvement in mAP@0.5 over the original YOLOv7 algorithm. These results demonstrate that the enhanced model surpasses existing algorithms in detecting small objects. [ABSTRACT FROM AUTHOR]

This study aims to address the problem in tracking technology in which targeted cruising ships or submarines sailing near the water surface are tracked at low frame rates or with some frames missing in the video image, so that the tracked targets have a large gap between frames, leading to a decrease in tracking accuracy and inefficiency. Thus, in this study, we proposed a water surface dynamic multi-target tracking algorithm based on the fusion of YOLOv7 and DeepSORT. The algorithm first introduces the super-resolution reconstruction network. The network can eliminate the interference of clouds and waves in images to improve the quality of tracking target images and clarify the target characteristics in the image. Then, the shuffle attention module is introduced into YOLOv7 to enhance the feature extraction ability of the target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of the DeepSORT algorithm to replace the distance matching of IOU to improve the target tracking accuracy. Simulation results showed that the algorithm proposed in this study has a good tracking effect, with an improvement of 9.4% in the improved YOLOv7 model relative to the mAP50-95 value and an improvement of 13.1% in the tracking accuracy in the DeepSORT tracking network compared with the SORT tracking accuracy. [ABSTRACT FROM AUTHOR]

Surface defect detection on wafers is crucial for quality control in semiconductor manufacturing. However, the complexity of defect spatial features, including mixed defect types, large scale differences, and overlapping, results in low detection accuracy. In this paper, we propose a CC-De-YOLO model, which is based on the YOLOv7 backbone network. Firstly, the coordinate attention is inserted into the main feature extraction network. Coordinate attention decomposes channel attention into two one-dimensional feature coding processes, which are aggregated along both horizontal and vertical spatial directions to enhance the network’s sensitivity to orientation and position. Then, the nearest neighbor interpolation in the upsampling part is replaced by the CAR-EVC module, which predicts the upsampling kernel from the previous feature map and integrates semantic information into the feature map. Two residual structures are used to capture long-range semantic dependencies and improve feature representation capability. Finally, an efficient decoupled detection head is used to separate classification and regression tasks for better defect classification. To evaluate our model’s performance, we established a wafer surface defect dataset containing six typical defect categories. The experimental results show that the CCDe-YOLO model achieves 91.0% mAP@0.5 and 46.2% mAP@0.5:0.95, with precision of 89.5% and recall of 83.2%. Compared with the original YOLOv7 model and other object detection models, CC-De-YOLO performs better. Therefore, our proposed method meets the accuracy requirements for wafer surface defect detection and has broad application prospects. The dataset containing surface defect data on wafers is currently publicly available on GitHub (https://github.com/ztao3243/Wafer-Datas.git).

With the construction of intelligent grids, unmanned aerial vehicle have been widely employed to inspect transmission lines. The inspection process generates a large amount of data, which requires a lightweight model to reduce computational overhead. Here, we propose an improved model based on YOLOv7-tiny with group-level pruning to reduce the model size, which achieves a balance between detection accuracy and speed. Firstly, we replace the activation function with the Funnel activation function to optimize the activation domain dynamically. Second, we introduce a lightweight DFC attention mechanism to enhance the ability of backbone to extract long-range features. Finally, we use adaptively spatial feature fusion network to reduce semantic degradation during feature fusion. We group the parameters according to their dependencies and use a consistent sparse approach to obtain parameter importance. The redundant parameter groups were pruned to achieve model light-weighting. Experimental results show that the improved model achieves 95.6% detection accuracy after pruning. Compared with YOLOv7-tiny, the computational complexity is reduced by 53% and the processing speed is increased by 48.1% to 73 frames per second. [ABSTRACT FROM AUTHOR]

To solve the problems of low accuracy, slow detection speed, and difficulty in deploying model parameters in surface defect detection of continuous casting production process, a lightweight surface defect detection algorithm YOLOv7-TSCR that integrates heavy parameterization and attention mechanism was proposed. Firstly, based on the Mish and SiLU activation functions and the SimAM attention mechanism, an improved high-efficiency layer aggregation module ELAN-S was constructed to effectively enhance the extraction of multi-scale defect features. Secondly, the C2f_RG module was designed to improve the feature fusion network, reducing the number of parameters while obtaining richer gradient flow information and enhancing feature fusion capabilities. Finally, based on the collected defect images from actual production, a dataset of casting defects was constructed and validated. The results show that YOLOv7-TSCR has significantly improved detection performance compared to other network models；With a reduced number of model parameters, the accuracy reaches 93.5%, the average accuracy increasesby 2.8%, and the detection speed reaches 120 FPS; The generalization comparison experiment on the NEU-DET public dataset proves that the algorithm has strong generalization. On the basis of ensuring high detection accuracy, the improved algorithm has a fast detection speed and a small number of parameters, which provides a technical reference for the efficient detection of surface defects in casting billets.

Abstract Aiming at the problems of error detection and missing detection in night target detection, this paper proposes a night target detection algorithm based on YOLOv7(You Only Look Once v7). The algorithm proposed in this paper preprocesses images by means of square equalization and Gamma transform. The GSConv(Group Separable Convolution) module is introduced to reduce the number of parameters and the amount of calculation to improve the detection effect. ShuffleNetv2_×1.5 is introduced as the feature extraction Network to reduce the number of Network parameters while maintaining high tracking accuracy. The hard-swish activation function is adopted to greatly reduce the delay cost. At last, Scylla Intersection over Union function is used instead of Efficient Intersection over Union function to optimize the loss function and improve the robustness. Experimental results demonstrate that the average detection accuracy of the proposed improved YOLOv7 model is 88.1%. It can effectively improve the detection accuracy and accuracy of night target detection.

During the packaging process, it is essential to detect the steel stamping characters inside the box to identify any missing or repeated characters. Currently, manual detection suffers from low efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was used to improve the efficiency of mapping steel stamping character recognition sequences. The experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with a processing efficiency of 10.5 ms per single frame. These findings align with the performance criteria for automatic recognition and analysis of steel stamping characters.

The gangue recognition technology is one of the key technologies in the intelligent construction of mines. To address the problem of low accuracy of the gangue recognition model caused by low illumination and high dust environment at the working face and the difficulty of recognizing small target gangue, a coal gangue image recognition model based on CSPNet-YOLOv7 target detection algorithm is proposed. Cross Stage Partial Network (CSPNet) is used to improve the backbone feature extraction network of YOLOv7 model, optimize the gradient information to reduce the network parameters, while Recursive Feature Pyramid (RFP) and Switchable Auto Convolution (SAC) to replace the simple up and down sampling and normal convolution modules in the neck feature extraction network, and to enhance the generalization ability of the network by using three migration training for feature learning of different widths and depths. The experimental results show that the CSPNet-YOLOv7 model has an average accuracy mean of 97.53%, an accuracy rate of 92.24%, a recall rate of 97.91%, an F1 score of 0.95, a model parametric number of 30.85×106, a floating point operation count of 42.15×109, and a frame rate of 24.37 f/s transmitted per second, Compared to the YOLOv7 model, the average mean accuracy is improved by 7.46%, and the number of parameters and floating point operations are reduced by 17.23% and 60.41%, respectively, compared to the FasterRCNN-Resnet50, YOLOv3, YOLOv4, MobileNet V2 -YOLOv4, YOLOv4-VGG, YOLOv5s models. The CSPNet-YOLOv7 model has the highest average accuracy mean for coal gangue identification, while the number of parameters and floating point operations is small, which has a good balance between identification accuracy and speed. Finally, the CSPNet-YOLOv7 model is validated through downhole field tests, providing an effective technical means for accurate coal gangue identification.

Abstract The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

To address the challenges of multi-scale objects, dense distributions, occlusions, and numerous small targets in UAV image detection, we present CMS-YOLOv7, a real-time target detection method based on an enhanced YOLOv7 model. Firstly, the detection layer P2 for small targets was added to YOLOv7 to enhance the detection ability of small and medium-sized targets, and the deep detection head P5 was taken out to mitigate the influence of excessive downsampling on small target images. The anchor frame was calculated by the K-means++ method. Using the concept of Inner-IoU, the Inner-MPDIoU loss function was constructed to control the range of the auxiliary border and improve detection performance. Furthermore, the CARAFE module was introduced to replace traditional upsampling methods, offering improved integration of semantic information during the image upsampling process and enhancing feature mapping accuracy. Simultaneously, during the feature extraction stage, a non-strided convolutional SPD-Conv module was constructed using space-to-depth techniques. This module replaced certain convolutional operations to minimize the loss of fine-grained information and improve the model's ability to extract features from small targets. Experiments on the UAV aerial photo dataset VisDrone2019 demonstrated that compared with the baseline YOLOv7 object detection algorithm, CMS-YOLOv7 achieved an improvement of 3.5% mAP@0.5, 3.0% mAP@0.5:0.95, and the number of parameters decreased by 18.54 M. The ability of small target detection was significantly enhanced. [ABSTRACT FROM AUTHOR]

Traffic mark detection and identification play a key character in the development of driverless and intelligent transportation systems, offering significant assistance in ensuring the safety of people's daily travels. However, the detection effect of traffic signs is affected by many target categories, small targets, and low recognition accuracy, making traffic sign detection more challenging than target detection in general scenarios. In this paper, an improved YOLOv7 network (YOLOv7-COORD) is entered. Foremost, increase CBAM attention module at the connection between backbone and neck network of YOLOv7 to enhance the expression ability of neural networks through the attention mechanism, emphasizing important features and ignoring minor features to enhance the efficiency and precision of the network. Secondly, By adding CoordConv before the upsampling of the neck and before the detection head output, the network can better feel the location message in the characteristic map. Finally, a detection head generated by the low-level, high-resolution characteristic map is added to enhance the recognition accuracy of small target object. The abundance of experimental data demonstrates that the impression of the improved YOLOv7-COORD model is superior to that of the original YOLOv7 model, and the average accuracy of (mAP@0.5) on TT100K datasets is 3.2% higher than that of YOLOv7, reaching 85.4%. In summary, the improved YOLOv7-COORD model can better detect targets in traffic sign images. [ABSTRACT FROM AUTHOR]

To enhance the quality of defect detection for Printed Circuit Board Assembly (PCBA) during electronic product manufacturing, this study primarily focuses on optimizing the YOLOv7-based method for PCBA defect detection. In this method, the Mish, a smoother function, replaces the Leaky ReLU activation function of YOLOv7, effectively expanding the network's information processing capabilities. Concurrently, a Squeeze-and-Excitation attention mechanism (SEAM) has been integrated into the head of the model, significantly augmenting the precision of small target defect detection. Additionally, considering angular loss, compared to the CIoU loss function in YOLOv7, the SIoU loss function in the paper enhances robustness and training speed and optimizes inference accuracy. In terms of data preprocessing, this study has devised a brightness adjustment data enhancement technique based on split-filtering to enrich the dataset while minimizing the impact of noise and lighting on images. The experimental results under identical training conditions demonstrate that our model exhibits a 9.9% increase in mAP value and an FPS increase to 164 compared to the YOLOv7. These indicate that the method proposed has a superior performance in PCBA defect detection and has a specific application value. [ABSTRACT FROM AUTHOR]

Enhancing security is currently a paramount concern for society, as traditional surveillance methods necessitate constant vigilance and monitoring of cameras, which can be inadequate. To address this issue, developing an automated security system capable of real-time detection of abnormal human activities and taking appropriate actions is imperative. This paper introduces a novel approach for classifying human actions in controlled environments by combining a support vector machine (SVM) with the deep learning model You Only Look Once (YOLOv7). The YOLOv7 model calculates the boundaries of detected targets, which are then input into the SVM to enhance classification accuracy. The results demonstrate superior classification performance compared to alternative models. In practical terms, the proposed method achieves a testing accuracy 94.24% in classifying human activities based on real-world data. This approach offers promise for preemptively identifying abnormal actions before they occur, paving the way for further advancements in security methods. [ABSTRACT FROM AUTHOR]

The detection of concealed suspicious objects in public places is a critical issue and a popular research topic. Terahertz (THz) imaging technology, as an emerging detection method, can penetrate materials without emitting ionizing radiation, providing a new approach to detecting concealed suspicious objects. This study focuses on the detection of concealed suspicious objects wrapped in different materials such as polyethylene and kraft paper, including items like scissors, pistols, and blades, using THz imaging technology. To address issues such as the lack of texture details in THz images and the contour similarity of different objects, which can lead to missed detections and false alarms, we propose a THz concealed suspicious object detection model based on SMR–YOLO (SPD_Mobile + RFB + YOLO). This model, based on the MobileNext network, introduces the spatial-to-depth convolution (SPD-Conv) module to replace the backbone network, reducing computational and parameter load. The inclusion of the receptive field block (RFB) module, which uses a multi-branch structure of dilated convolutions, enhances the network's depth features. Using the EIOU loss function to assess the accuracy of predicted box localization further optimizes convergence speed and localization accuracy. Experimental results show that the improved model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.9% and 89.4%, respectively, representing improvements of 0.2% and 1.8% over the baseline model. Additionally, the detection speed reached 108.7 FPS, an improvement of 23.2 FPS over the baseline model. The model effectively identifies concealed suspicious objects within packages, offering a novel approach for detection in public places. [ABSTRACT FROM AUTHOR]

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Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional squeeze-and-excitation convolution (SEConv) to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. In addition, the SEConv-based spatial pyramid pooling and connected spatial pyramid convolution (SPPCSPC) module extracts image features. It improves detection accuracy while the feature fusion section integrates the convolutional block attention module (CBAM) hybrid attention network, forming the convolutional block attention module Concat (CBAMCAT) module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model's feature fusion capabilities. Experiments on public remote sensing datasets reveal that the proposed SE-CBAM-YOLOv7 improves detection accuracy by 0.5% and the mAP value by 1.7% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging. [ABSTRACT FROM AUTHOR]

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This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-greenripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layerbased adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province--navel orange, Ehime Jelly orange, and Harumi tangerine--YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into prosthodontics has significantly improved diagnostic accuracy and treatment planning precision. This research evaluates the efficacy of four prominent object detection algorithms--YOLOv7, YOLOv8, AdaBoost, and RCNN--in identifying dental implants from radiographic images. Using a custom dataset containing over 5000 images collected from dental clinics, the study conducts a thorough assessment of each algorithm's performance. The proposed approach involves data processing and model training. This comparative analysis provides valuable insights into the performance characteristics of each algorithm, particularly regarding dental implant identification. Such insights can assist prosthodontists in making informed decisions regarding algorithm selection for clinical implementation, ultimately enhancing patient care and treatment outcomes in the field of dental prosthetics. Findings suggest that YOLOv7 and YOLOv8 excel in both speed and accuracy of dental implant identification, with AdaBoost also performing admirably, although with slightly slower processing times. However, RCNN, despite its precise localization capabilities, demonstrates relatively slower processing speeds. Evaluation metrics unveil varying levels of accuracy among the models, ranging from 77% to 92%. Furthermore, these findings contribute to the ongoing advancements in AI-assisted dental care, promising improved efficiency and precision in treatment planning and execution for better patient outcomes. [ABSTRACT FROM AUTHOR]

Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO (You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model's ability to extract citrus' features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors. [ABSTRACT FROM AUTHOR]

The task of detecting marine target organisms has always been a challenging issue, despite the numerous machine learning detection methods proposed to improve precision. The underwater image blurriness caused by irregular light absorption and water quality remains a major obstacle to achieving accurate detection. This results in high misalignment rates and poor underwater scene recognition capabilities for detecting underwater targets. To address this, we put forward a YOLOv7-RNCA underwater target detection technology based on improvements to YOLOv7. This model adds residual modules and coordinate attention mechanisms (CA) at the end of the backbone network, as well as incorporating partial convolution (PConv) modules. The combination of these three components makes the model more precise during the detection process while reducing unnecessary computation and memory access. This allows for better optimization during deep network training and preserves more feature information. Additionally, we reconstructed the SPPCSPC structure and incorporated a global attention mechanism (GAM) to form the SPPCSPC-GAM module in the neck network, which improves the performance of the convolutional neural network (CNN) and ensures good data capabilities and robustness during training, thereby enhancing the target detection ability. We also improved the neck ELAN module by introducing PConv convolution modules, which continuously enhance network learning abilities without disrupting the original gradient path. The introduction of the PConv module reduces redundant computation and memory access, making the ELAN-PConv module more effective at extracting spatial features. Our outcomes of experimentation indicate YOLOv7-RNCA network an average precision of 86.6% on the URPC dataset, outperforming existing methods in accuracy detection and demonstrating great potential as a promising solution for marine target monitoring tasks. [ABSTRACT FROM AUTHOR]

Despite the rapid advances in deep learning (DL) for object detection, existing techniques still face several challenges. In particular, object detection in adverse weather conditions (AWCs) requires complex and computationally costly models to achieve high accuracy rates. Furthermore, the generalization capabilities of these methods struggle to show consistent performance under different conditions. This work focuses on improving object detection using You Only Look Once (YOLO) versions 5, 7, and 9 in AWCs for autonomous vehicles. Although the default values of the hyperparameters are successful for images without AWCs, there is a need to find the optimum values of the hyperparameters in AWCs. Given the many numbers and wide range of hyperparameters, determining them through trial and error is particularly challenging. In this study, the Gray Wolf Optimizer (GWO), Artificial Rabbit Optimizer (ARO), and Chimpanzee Leader Selection Optimization (CLEO) are independently applied to optimize the hyperparameters of YOLOv5, YOLOv7, and YOLOv9. The results show that the preferred method significantly improves the algorithms' performances for object detection. The overall performance of the YOLO models on the object detection for AWC task increased by 6.146%, by 6.277% for YOLOv7 + CLEO, and by 6.764% for YOLOv9 + GWO. [ABSTRACT FROM AUTHOR]

Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model's feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CIoU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model's detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations. [ABSTRACT FROM AUTHOR]

It has been a challenge to obtain accurate detection results in a timely manner when faced with complex and changing surface target detection. Detecting targets on water surfaces in real-time can be challenging due to their rapid movement, small size, and fragmented appearance. In addition, traditional detection methods are often labor-intensive and time-consuming, especially when dealing with large water bodies such as rivers and lakes. This paper presents an improved water surface target detection algorithm that is based on the YOLOv7 (you only look once) model to enhance the performance of water surface target detection. We have enhanced the accuracy and speed of detecting surface targets by making improvements to three key structures: the network aggregation structure, the pyramid pooling structure, and the down-sampling structure. Furthermore, we implemented the model on mobile devices and designed a detection software. The software enables real-time detection through images and videos. The experimental results demonstrate that the improved model outperforms the original YOLOv7 model. It exhibits a 6.4% boost in accuracy, a 4.2% improvement in recall, a 4.1% increase in mAP, a 14.3% reduction in parameter counts, and archives the FPS of 87. The software has the ability to accurately recognize 11 typical targets on the water surface and demonstrates excellent water surface target detection capability. [ABSTRACT FROM AUTHOR]

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7. [ABSTRACT FROM AUTHOR]

At present, catch statistics in the pelagic fishery industry rely mainly on manual counting methods. However, this method suffers from low statistical accuracy and insufficient timeliness. An automatic tuna counting approach based on ByteTrack and YOLOv7-Tuna is presented in this research. The method selects YOLOv7 as the base model, adopts DySnakeConv to obtain more temporal features, combines it with CoordConv to enhance the location-awareness ability of the model, and introduces DyHead to suppress the interference of complex backgrounds. The experimental results show that YOLOv7-Tuna outperforms YOLOv7 in terms of precision by 5.2%, recall by 3.1%, mAP@0.5 by 0.5%, and mAP@0.5:0.95 by 10%. Furthermore, the ByteTrack algorithm was employed to achieve real-time tracking of targets, with specific counting areas added. The results indicate that the counting error of this method decreased to 3.1%. It can effectively accomplish automatic counting tasks for tuna, providing a new solution for the automatic quantification of catch in the offshore fishing industry. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the grid meter dial, precise feature extraction is becoming more and more difficult. Many automatic recognition solutions have been proposed for grid meter readings. However, traditional inspection methods cannot guarantee detection accuracy in complex environments. So, deep-learning methods are combined with grid meter recognition. Existing recognition systems that utilize segmentation models exhibit very high computation. It is challenging to ensure high real-time performance in edge computing devices. Therefore, an improved meter recognition model based on YOLOv7 is proposed in this paper. Partial convolution (PConv) is introduced into YOLOv7 to create a lighter network. Different PConv introduction locations on the base module have been used in order to find the optimal approach for reducing the parameters and floating point of operations (FLOPs). Meanwhile, the dynamic head (DyHead) module is utilized to enhance the attention mechanism for the YOLOv7 model. It can improve the detection accuracy of striped objects. As a result, this paper achieves mAP 50 v a l of 97.87% and mAP 50 : 90 v a l of 62.4% with only 5.37 M parameters. The improved model's inference speed can reach 108 frames per second (FPS). It enables detection accuracy that can reach ± 0.1 degrees in the grid meter. [ABSTRACT FROM AUTHOR]

Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques. Previous work has focused on symbol-based traffic signals, where popular single-task learning models have been trained and tested. Therefore, several comparisons have been conducted to select accurate single-task learning models. For further improvement, these models are employed in a multi-task learning approach. Indeed, multi-task learning algorithms are built by sharing the convolutional layer parameters between the different tasks. Hence, for the multi-task learning approach, different experiments have been carried out using pre-trained architectures like, for instance, InceptionResNetV2 and DenseNet201. A range of traffic signs and traffic lights are employed to validate the designed model. An accuracy of 99.07% is achieved when the entire network has been trained. To further enhance the accuracy of the model for traffic signs obtained from the street, a region of interest module is added to the multi-task learning module to accurately extract the traffic signs available in the image. To check the effectiveness of the adopted methodology, the designed model has been successfully tested in real-time on a few Riyadh highways. [ABSTRACT FROM AUTHOR]

Collision accidents of construction vehicles frequently occur during the construction of building projects. It is difficult to warn of collision risk efficiently by using the safety control methods of on-site inspection and viewing surveillance video. Therefore, a novel collision risk warning model for construction vehicles is proposed in this paper. The model comprises a state awareness module and a collision risk assessment module. In the state awareness module, the YOLOv7 algorithm is used to identify and locate construction vehicles. The DeepSORT algorithm is used for the real-time tracking of construction vehicles. Finally, the state awareness module obtains a wealth of sensing information and provides it to the collision risk assessment module. In the collision risk assessment module, the speed assessment method and the minimum safety distance assessment method of construction vehicles are innovatively proposed and compiled. In the speed assessment method, the speed of construction vehicles is calculated and speeding warnings are performed using the transformation matrix between latitude and longitude coordinates and image pixel coordinates. In the minimum safety distance assessment method, the original surveillance picture is converted into a bird's eye view (BEV) of the world coordinate system through inverse perspective mapping (IPM). Then the distance of all construction vehicles is traversed, and the visual warning of the minimum safe distance is provided. The effectiveness and robustness of this collision risk warning model were verified by taking three practical projects in different construction stages as examples. The research results show that the mean average precision (mAP@0.5:0.95) of the construction vehicles collision risk warning model can reach 89.10%, and it has a good tracking effect and an efficient collision risk warning strategy. [ABSTRACT FROM AUTHOR]

The smart classroom is a fully automated classroom where repetitive tasks, including attendance registration, are automatically performed. Due to recent advances in artificial intelligence, traditional attendance registration methods have become challenging. These methods require significant time and effort to complete the process. Therefore, researchers have sought alternative ways to accomplish attendance registration. These methods include identification cards, radio frequency, or biometric systems. However, all of these methods have faced challenges in safety, accuracy, effort, time, and cost. The development of digital image processing techniques, specifically face recognition technology, has enabled automated attendance registration. Face recognition technology is considered the most suitable for this process due to its ability to recognize multiple faces simultaneously. This study developed an integrated attendance registration system based on the YOLOv7 algorithm, which extracts features and recognizes students' faces using a specially collected database of 31 students from Mustansiriyah University. A comparative study was conducted by applying the YOLOv7 algorithm, a machine learning algorithm, and a combined machine learning and deep learning algorithm. The proposed method achieved an accuracy of up to 100%. A comparison with previous studies demonstrated that the proposed method is promising and reliable for automating attendance registration. [ABSTRACT FROM AUTHOR]

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1 1 convolutional architecture are added to the DCN module to improve the model's perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. [ABSTRACT FROM AUTHOR]

Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in object detection due to the rapid development of deep learning. Multi-scale targets and complex scenes increase the likelihood of false positives and missed detections, which can affect the accuracy of the detection. To address this issue, this study proposes YOLOv7-DSE. It is a small complex target scene detection network based on the improved YOLOv7. Also, we have created a private dataset of safety equipment. First, we enhanced the ELAN and MP backbone networks. Backbone is replaced by ordinary convolution by the depthwise separable convolution. We enabled the backbone network to extract deeper image features without increasing the amount of parameters and computation. Simultaneously, the model incorporates the EIOU loss function to improve its convergence speed and positioning effect. Secondly, we propose a new ELAN-SPD structure in the head network. Based on the ELAN structure, a space-to-depth convolutional layer is added to fully downsample the feature map, preserving all learnable features. Our network model can better detect objects with significant size differences faced with complex scenes. YOLOv7-DSE achieved the mAP of 82.38%, surpassing the original YOLOv7 with 2.64%. The YOLOv7-DSE model has a minor size compared to the baseline model. Our improvement has reduced the model parameters by 22.4%. [ABSTRACT FROM AUTHOR]

Plant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, and mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, and object detection. Deep Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate object detection. In this study, YOLOv5, YOLOv7 and YOLOv8 models were trained on CCL'20 dataset for citrus disease detection. Data augmentation techniques such as image translation, image scaling, flip, mosaic augmentations were implemented to improve the models' performance during training phase. The model performance was evaluated using metric such as Mean Average Precision at 50% to 95% Intersection over Union score i.e. mAP@50-95. The results show that YOLOv8 model performs better than other variants and offers significant improvements over the benchmark performance from previous studies. The final hyper-parameter tuned model achieved 96.1% mAP@50-95 on testing data for citrus disease detection and mAP@50-95 of 95.3%, 96.0% and 97.0% for detection of Anthracnose, Melanose and Bacterial Brown Spot diseases, respectively. The trained model was able to detect single and multiple instances of same or different disease in an image showing the potential of recent YOLO models. The trained YOLOv8 model is deployed on Roboflow platform. [ABSTRACT FROM AUTHOR]

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection. [ABSTRACT FROM AUTHOR]