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Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals' output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm. [ABSTRACT FROM AUTHOR]

Deep learning is proliferating within the field of computer vision. Road damage detection technology, as its offshoot, already plays vital role in road maintenance and traffic safety. With road damage such as potholes and cracks, accurate and efficient detection results are essential for timely road safety repair and maintenance. Therefore, road damage detection algorithms based on deep learning have attracted wide attention. YOLOv5 is an advanced target detection algorithm known for its efficient detection speed and good accuracy. However, there is still room for further improvement in its performance for road damage detection. The ability of multi-scale damage detection and spatial structure capture is not perfect. Therefore, this paper proposes three improvement points to improve the accuracy of road damage detection based on YOLOv5. The first introduced module is the Non-linear Spatial Pyramidal Pooling-Fast (NSPPF) module. This module allows for better capture of detailed features of road damage areas. Non-linear transformation and fast pyramid operation improve the sensing ability and multi-scale damage detection ability. Secondly, a combination of the CoordConv and SK attention modules is constructed. The CoordConv module fuses coordinate information with features to provide a more spatially informed representation. The SK attention module also learns correlations between global and local features, enhancing the model's ability to detect damages at different scales. This paper can better capture road injuries' spatial structure and context information by combining these two modules. Finally, experimental results on the RDD2020 dataset demonstrate the effectiveness of our model. Compared to the baseline model, the proposed improvement algorithm increases the accuracy by 2.2%, resulting in a mAP of 58.2%. This demonstrates its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

The traditional infrared inspection of substations is usually done by employees with handheld infrared equipment, but the traditional inspection efficiency is not high, and some of the inspection locations have harsh environments, high voltage levels, and high‐risk factors. Given the above problems, this paper proposes an improved defect detection algorithm for substations based on YOLO v7 to solve the difficult problem of infrared inspection of substations. First, the lighter SE‐GhostNetV2 feature extraction network is designed to reduce the computation volume; second, the de‐weighted BIFPN structure is designed for the substation infrared image features to improve the ability of information interaction; finally, the new C‐NWD Loss is proposed to realize the detection of smaller substation equipment and reducing missed detections. The experimental results show that the hybrid accuracy of the algorithm designed in this paper is improved from 92.1% to 96.6%, the model parameters are reduced by 40.1%, the computation amount is reduced by 65%, the detection speed is improved by 87 FPS, and the false detection and missed detections is less, which can achieve real‐time infrared inspection of substations and solve the difficulties existing in traditional substation infrared detection. [ABSTRACT FROM AUTHOR]

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model's adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments. [ABSTRACT FROM AUTHOR]

Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3/walking_xyz and fr3/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively. [ABSTRACT FROM AUTHOR]

Because of the limitations of current PCB detection methods, such as low detection accuracy and high rate of false and missed detection, a PCB defect detection algorithm based on improved RetinaNet is proposed, that is, RetinaNet-line. Firstly, the algorithm uses PVT v2B2-Hi as the backbone network of the whole model, which can not only solve the deep feature redundancy caused by the residual network in RetinaNet but also improve the feature extraction ability of the network. Then, the CARAFE up-sampling module is used to enrich the semantic features of defective targets and promote the discrimination ability of the algorithm to defective targets. Finally, HSV color space is used to process the input feature image, and the color distribution is expanded by random perturbation in H, S, and V channels, with the data enhancement completed. In this paper, experiments are conducted on the publicly shared PCB defect detection dataset from the Intelligent Open Laboratory of Peking University, and the results show that the improved algorithm achieves 94.2% in the mAP index, which is better than other networks. [ABSTRACT FROM AUTHOR]

The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets [ABSTRACT FROM AUTHOR]

Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of RGB and infrared images can effectively address the challenges of complex and changing road scenes, improving the detection performance of current algorithms in complex scenarios. However, previous multispectral detection algorithms suffer from issues such as poor fusion of dual-mode information, poor detection performance for multi-scale objects, and inadequate utilization of semantic information. To address these challenges and enhance the detection performance in complex road scenes, this paper proposes a novel multispectral object detection algorithm called MRD-YOLO. In MRD-YOLO, we utilize interaction-based feature extraction to effectively fuse information and introduce the BIC-Fusion module with attention guidance to fuse different modal information. We also incorporate the SAConv module to improve the model's detection performance for multi-scale objects and utilize the AIFI structure to enhance the utilization of semantic information. Finally, we conduct experiments on two major public datasets, FLIR_Aligned and M3FD. The experimental results demonstrate that compared to other algorithms, the proposed algorithm achieves superior detection performance in complex road scenes. [ABSTRACT FROM AUTHOR]

For airborne radar, detecting a low–slow–small (LSS) target is a hot and challenging topic, which results from the rapidly increasing number of non-cooperative flying LSS targets becoming of widespread concern, and the low signal-to-clutter ratio (SCR) of LSS targets results in the targets being particularly easily overwhelmed by the clutter. In this paper, a novel light gradient boosting machine (LightGBM)-based LSS target detection algorithm for airborne radar is proposed. The proposed method, based on the current real-time clutter environment of the range cell to be detected, firstly designs a specific real-time space-time LSS target signal repository with special dimensions and structures. Then, the proposed method creatively designs a new fast-built real-time training feature dataset specifically for the LSS target and the current clutter, together with a series of unique data transformations, sample selection, data restructuring, feature extraction, and feature processing. Finally, the proposed method develops a unique machine learning-based LSS target detection classifier model for the designed training dataset, by fully excavating and utilizing the advantages of the ensemble decision trees-based LightGBM. Consequently, the pre-processed data in the range cell of interest are classified using the proposed algorithm, which achieves LSS target detection by evaluating the output results of the designed classifier. Compared with the traditional classical target detection methods, the proposed algorithm is capable of providing markedly superior performance for LSS target detection. With an appropriate computational time, the proposed algorithm attains the highest probability of detecting LSS targets under the low SCR. The simulation outcomes and detection results with the experimental data are employed to validate the effectiveness and merits of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate. [ABSTRACT FROM AUTHOR]

Due to the robustness of skeleton data to human scale, illumination changes, dynamic camera views, and complex backgrounds, great progress has been made in skeleton‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio‐temporal dependencies of non‐Euclidean data such as human skeleton graphs, and the autoencoder based on this basic unit is widely used to model sequence features. However, due to the limitations of the convolution kernel, the model cannot capture the correlation between non‐adjacent joints, and it is difficult to deal with long‐term sequences, resulting in an insufficient understanding of behaviour. To address this issue, this paper applies the Transformer to the human skeleton and proposes the Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder (STEGT‐AE) to improve the capability of modelling. In addition, the multi‐memory model with skip connections is employed to provide different levels of coding features, thereby enhancing the ability of the model to distinguish similar heterogeneous behaviours. Furthermore, the STEGT‐AE has a single encoder‐double decoder architecture, which can improve the detection performance by the combining reconstruction and prediction error. The experimental results show that performances of STEGT‐AE is significantly better than other advanced algorithms on four baseline datasets. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, electric power equipment detection systems are developing in the direction of artificial intelligence. To achieve good automatic detection results, a high-quality and speedy algorithm is designed to intelligently detect indoor switchgear components in substations. This proposed method can detect the status of components based on image processing technology, which belongs to the field of condition monitoring. In this paper, the targets to be detected include multi-colour buttons or lights and the ammeters or voltmeters of the electrical switchgear. Two hybrid improved algorithms are used to optimise the you only look once v5s (YOLOv5s) network framework for increasing the detection speed and performance. Firstly, deeper feature map extraction is achieved using HorNet recursive gated convolution to replace the original C3 module for more efficient results. Then, a bidirectional feature pyramid network (BiFPN) algorithm is used to achieve the bidirectional propagation of feature information in the feature pyramid. This method can promote better fusion of feature information at different levels and help to convey feature and location information in the image. Finally, the improved YOLOv5s-BH model is used to detect the targets in substations. The experimental results show that the proposed method provides encouraging detection results for indoor switchgear components in substations. [ABSTRACT FROM AUTHOR]

Substation electrical equipment generates a massive number of infrared images during operation. However, the overall quality of the infrared images is low and it lacks image detail information. When using traditional target detection algorithms for detection, feature extraction poses great difficulties. Therefore, to address this problem, this paper proposes a target detection algorithm based on the improved faster region-based convolutional neural network (Faster R-CNN). It achieves the correct identification of different types of electrical equipment in infrared images. First, the algorithm improves the backbone network of Faster R-CNN for feature extraction. An InResNet structure is proposed to replace the residual block structure of the original ResNet-34 network, which enhances the richness of feature extraction. Second, the rectified linear unit activation function in the original feature extraction network is replaced by the exponential linear unit activation function, and group normalization is used instead of batch normalization as the network normalization method. Then, the dense connection structure is introduced into the ResNet-34 network, and the whole network is called residual dense connection network. Finally, the improved Faster R-CNN is compared to the original Faster R-CNN, a single-shot multibox detector, and you only look once v3 plus spatial pyramid pooling. The experimental results show that the improved algorithm has the highest mean average precision and average recall for most of the substation electrical equipment in infrared images. Moreover, from the confidence level of the detected electrical equipment and the accuracy of the prediction box, the improved Faster R-CNN has the best performance. [ABSTRACT FROM AUTHOR]

Multimedia data have increased dramatically today, making the distinction between desirable information and other types of information extremely important. Speech/music discrimination is a field of audio analytics that aims to detect and classify speech and music segments in an audio file. This paper proposes a novel feature extraction method called Long-Term Multi-band Frequency-Domain Mean-Crossing Rate (FDMCR). The proposed feature computes the average frequency-domain mean-crossing rate along the frequency axis for each of the perceptual Mel-scaled frequency bands of the signal power spectrum. In this paper, the class-separation capability of this feature is first measured by well-known divergence criteria such as Maximum Fisher Discriminant Ratio (MFDR), Bhattacharyya divergence, and Jeffreys/Symmetric Kullback–Leibler (SKL) divergence. The proposed feature is then applied to the speech/music discrimination (SMD) process on two well-known speech-music datasets—GTZAN and S &S (Scheirer and Slaney). The results obtained on the two datasets using conventional classifiers, including k-NN, GMM, and SVM, as well as deep learning-based classification methods, including CNN, LSTM, and BiLSTM, show that the proposed feature outperforms other features in speech/music discrimination. [ABSTRACT FROM AUTHOR]

With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer. [ABSTRACT FROM AUTHOR]

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages. [ABSTRACT FROM AUTHOR]

To overcome the problem of the high initial position of the point cloud required by the traditional Iterative Closest Point (ICP) algorithm, in this paper, we propose a point cloud registration method based on normal vector and directional histogram features (SHOT). Firstly, a hybrid filtering method based on the voxel idea is proposed and verified using the measured point cloud data, and the noise removal rates of 97.5%, 97.8%, and 93.8% are obtained. Secondly, in terms of feature point extraction, the original algorithm is optimized, and the optimized algorithm can better extract the missing part of the point cloud. Finally, a fine alignment method based on normal vector and directional histogram features (SHOT) is proposed, and the improved algorithm is compared with the existing algorithm. Taking the Stanford University point cloud data and the self-measured point cloud data as examples, the plotted iteration-error plots can be concluded that the improved method can reduce the number of iterations by 40.23% and 37.62%, respectively. [ABSTRACT FROM AUTHOR]

Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network's bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy. [ABSTRACT FROM AUTHOR]

Deep learning shows great potential in low-light image enhancement, which can improve image brightness and contrast while keeping image natural. However, due to the lack of prior knowledge extracted manually or excessive amplification of noise, these methods result in poor quality of enhanced images. To solve these challenging problems, this paper proposes a dual-branch grayscale image enhancement network based on dense residual and attention mechanism. Low-light grayscale image is used as input. Firstly, features that fuse deep and shallow information are extracted through dense residual convolution branch network; secondly, texture features are extracted through U-Net branch network combined with attention mechanism; then the extracted features are integrated, and finally luminance is adjusted by a brightness adjustment module to output an enhanced grayscale image. In addition, a joint loss function is designed to measure the network training loss from brightness, texture, contrast and noise. A large number of quantitative and qualitative experiments on LOL and VE-LOL datasets show that the proposed method improves Peak Signal to Noise Ratio Index and Structural Similarity Index by 19.65 - 59.76% and 5.61 - 85.53%, respectively, compared with EnlightenGAN, KinD++, RUAS, LLFlow, etc. The proposed method is superior to the most famous methods, thanks to the deep feature extraction and fusion capability of dense residual convolutional network and texture extraction capability of U-Net network combined with attention mechanism. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module; all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective. [ABSTRACT FROM AUTHOR]

Aiming at the problem that feature extraction is inaccurate and the reconstruction image is not natural enough in blind super-resolution reconstruction, this paper proposed an image blind super-resolution reconstruction based on degradation aware and sequence residuals. This paper proposed a mini-residual group combined degeneration aware and sequence residuals as the backbone network. Then the method constructed a symmetrical enhanced multi-scale residual block. In the image reconstruction part, this paper used the bottleneck attention module and the sub-pixel convolutional module to emphasize the multi-dimensional elements of the image. Finally, the method made a global residual connection. Compared with the current representative algorithm DASR, experiments show that the PSNR and SSIM of the proposed algorithm are improved 0.145dB and 0.0014 on Set14×2, and the PSNR of the proposed algorithm is improved 1.898dB and 0.252dB on Set14×3/4, respectively. The proposed algorithm achieves better performance than several current image super-resolution algorithms on five standard test sets. [ABSTRACT FROM AUTHOR]

During the replanting operation of a seedling tray, the end-effector needs to repeatedly grab the qualified plug seedlings in the supply tray and release them to the target tray for replanting, and in the process of grasping, the end-effector may cause some mechanical damage to the plug seedlings, thus affecting their quality. Therefore, in order to be able to adjust the position of the hand claw grasping point according to the morphological characteristics of the plug seedlings and select the optimal grasping point, this paper proposes research on the optimal grasping angle algorithm for plug seedlings based on machine vision. Firstly, a rotatable three-jaw end-effector is designed, which uses a three-jaw structure for grasping the burrowing seedlings. The three claws are driven with a telescopic cylinder to carry out clamping and relaxing actions. The rotation of the three claws is controlled with the stepper motor to adjust the optimal grasping position. Secondly, based on the pre-processing of an image of the hole tray seedling, the extraction of feature points in the region of interest, and the calculation of localization, the angle between the angular bisector of the cotyledon leaf blade of the hole tray seedling and the horizontal positive direction is solved. In this paper, two methods are designed to calculate the coordinates of feature points: one is the geometric method and the other is the center-of-mass method. Finally, the optimal grasping angle is calculated by analyzing the angle between the angular bisector of the cotyledon leaf blade and the horizontal positive direction of the cavity seedlings. According to the test, the average calculation error of the proposed algorithm is 3.12 degrees, and the average calculation time is 0.512 sec/sheet, which meet the requirements of the replanting operation. [ABSTRACT FROM AUTHOR]

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research. [ABSTRACT FROM AUTHOR]

Most of the Encryption-then-Compression (ETC) methods can only obtain several limited fixed compression ratios. However, arbitrary encryption compression ratios instead of limited fixed compression ratios are more suitable for practical requirements. To this end, this paper proposed a lossy compression algorithm for encrypted color images with arbitrary compression ratios. It combines the uniform and random downsampling to compress the encrypted images, obtaining arbitrary compression ratios of an encrypted image. The receiver receives the compressed sequence of the encrypted image and obtains the decrypted image by decompression and decryption. The proposed scheme then characterizes the lossy reconstruction of the original image from the decrypted image as an optimization problem with the downsampling compression-based constraint. This scheme designs a convolutional neural network-based image reconstruction model for lossy ETC to resolve this problem, which is denoted the ETC-oriented Reconstruction Network (ETRN). ETRN consists of shallow feature extraction (SFE), residual in residual (RIR), residual content supplementation (RCS), and down-sampling constraint (DC). The experimental simulation results show that the encrypted color image lossy compression algorithm proposed in this paper can obtain excellent compression and reconstruction performance, which fully demonstrates the feasibility and effectiveness of this method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient feature extraction ability of deep clustering algorithm on multivariate time series data( MTS), This paper proposes a new deep clustering structure model (MDTC. In order to extract the key features of MTS and realize dimensionality reduction, one-dimensional convolution learns the attribute and temporal dimension feature representation of MTS and the autoencoder structure composed of network layers such as recurrent neural network; To improve the model’s ability to represent temporal features, this paper proposes MCBAM temporal attention module, which is used to enhance the representation features of different time periods in the MTS sequence. This paper conducts experiments on nine publicly available UEA multivariate time series datasets, compared with other autoencoders, the autoencoder structure of the model improves by 2%-9% on 7 data sets; Compared with other attention modules, the MCBAM module of the model improves by 0.3%-2% on the six datasets. Experiments show the effectiveness of the MDTC model structure and MCBAM module, and the model has excellent performance compared with other clustering algorithms. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

Nowadays, physical dance is widely spread in the society as an emerging sport. Dance movement is favored by people because of its unique social function and fitness effect. For dance teaching, dance movement analysis can help optimize and improve the existing dance movements and the understanding and inheritance of traditional dance movements. With the rise of online teaching, intelligent identification and analysis of dance movements can promote the better development of sports dance teaching. However, the relevant research in this area is still very scarce. As the basis of this kind of research, there is an urgent need for dance motion recognition technology. Based on this background, this paper by introducing neural network algorithm for dance teaching sports image special recognition design, the algorithm can combine feature extraction technology to process video, extract the dance movements in the target data set, then for the extraction of cumulative feature extraction operation, in order to accumulate all the collected target features, so as to further complete the gradient histogram acquisition. Through the design experimental test, the cumulative feature image extraction results obtained through the algorithm are obviously better than the traditional image recognition results, so the design rationality and effectiveness of the algorithm are proved, and the sports dance teaching can be specially assisted. This paper designs an effective auxiliary image recognition algorithm by introducing the neural network algorithm into the field of sports dance teaching. [ABSTRACT FROM AUTHOR]

The detection algorithm commonly misses obscured pedestrians in traffic scenes with a high pedestrian density because mutual occlusion among pedestrians reduces the prediction box score of the concealed pedestrians. The paper uses the YOLOv7 algorithm as the baseline and makes the following three improvements by investigating the variables influencing the detection method's performance: First, the backbone network of the YOLOv7 algorithm is replaced with the lightweight feature extraction network Mobilenetv3 since the pedestrian detection algorithm frequently needs to be deployed in driverless mobile, which requires a fast operating speed of the algorithm; second, a high-resolution feature pyramid structure is suggested for the issue of missed detection of hidden pedestrians, which upscales the feature maps generated from the feature pyramid to increase the resolution of the output feature maps and introduces shallow feature maps to strengthen the distinctions between adjacent sub-features to enhance the network's ability to extract features for the visible area of hidden pedestrians and small-sized pedestrians in order to produce deeper features with greater differentiation for pedestrians; and the third is to suggest a detection head based on an attention mechanism that is employed to lower the confidence level of target neighboring sub-features, lower the quantity of redundant detection boxes, and lower the following NMS computation. The mAP of the suggested approach in this work achieves 89.75%, which is 9.5 percentage points better than the YOLOv7 detection algorithm, according to experiments on the CrowdHuman pedestrian-intensive dataset. The algorithm proposed in this paper can considerably increase the detection performance of the detection algorithm, particularly for obscured pedestrians and small-sized pedestrians in the dataset, according to the experimental effect plots. [ABSTRACT FROM AUTHOR]

[Objectives]Aiming at the difficulty of identifying pests and diseases in the real environment of bergamot in the field, this paper proposed a method for detecting pests and diseases of bergamot based on improved SSD(single shot MultiBox detector)algorithm—SSD-Res50-3C algorithm. [Methods]The backbone network partially replaced the original VGG16 network with the ResNet50 network, and enhanced the model's ability to extract the characteristics of pests and diseases of bergamot in the real background of the field. A lightweight and efficient feature fusion module was added before predicting the feature layer to improve the multi-scale feature fusion capability of the SSD algorithm, and further improved the anti-interference ability of the SSD algorithm in the real background of the field. [Results]The mean average precision of SSD-Res50-3C algorithm was 92.86%, which was 6.61% higher than that of the original SSD algorithm, and the FPS(frames per second)reached 64.1. Compared with YOLO v3, YOLO v4, YOLO v5x6, Faster R-CNN, and EfficientDet-D3 models, the mean average precision of SSD-Res50-3C algorithm was 6.41%, 2.01%, 0.79%, 0.58%, and 5.10%, respectively, and FPS was 16.20, 40.28, 24.40, 36.20, and 54.84, respectively. [Conclusions]The pests and diseases detection method of bergamot based on the improved SSD algorithm proposed in this paper could weaken the interference information of the real environment in the field, accurately identify the target of pests and diseases of bergamot in the real environment of the field, and provide a new idea for the detection of pests and diseases of bergamot in the real environment of the field. [ABSTRACT FROM AUTHOR]

In recent years, highway accidents occur frequently, the main reason is that there is always foreign body invasion on the highway, which makes people unable to respond to emergencies in time. In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the accuracy of object detection. Finally, a lightweight method was proposed to reduce the computational complexity. We compare the algorithm in this paper with existing algorithms, the experimental results showed that: On the Visdrone dataset (small size targets), (a) the CS-YOLO was 3.6% more accurate than the YOLO v8. (b) The CS-YOLO was 1.2% more accurate than the YOLO v8 on the Tinypersons dataset (minimal size targets). (c) CS-YOLO was 1.4% more accurate than YOLO v8 on VOC2007 data set (normal size). [ABSTRACT FROM AUTHOR]

Aiming at the problems of low recognition accuracy and large calculation amount of age estimation, this paper proposes a staged single aggregation backbone network VoVNetv4. In order to improve the feature extraction ability of the network, the Regional One-Shot Aggregation (ROSA) module is proposed in the network for regional encapsulation of the feature module. The classifier adopts the COnsistent RAnk Logits (CORAL) framework, which can solve the problem of inconsistency of classifiers in ordered regression CNNs for multiple binary classification tasks. This paper conducts experimental verification on the Asian Face Age Dataset (AFAD) and compares it with other research methods. The experimental results show that VoVNetv4 can effectively improve the feature extraction and detection ability of the network and improve the detection efficiency of age estimation. [ABSTRACT FROM AUTHOR]

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis of object detection algorithms using phase and statistical texture features. Robust metrics like accuracy and precision are used to perform the evaluation. Feature extraction is one of the prime requirements to represent an object. This paper provides a comprehensive and broad framework to phase feature and statistical feature extraction techniques and compares their performance in terms of accuracy and precision. In phase-based approach of object detection group of phase features are used to model the background. Whereas we represented the statistics of object by calculating GLCM features of the block images in statistical feature extraction methods. Support Vector machine algorithm is used to classify object. This method is developed to achieve higher accuracy and speed. Performance analysis of these two methods shows that phase feature is robust to illumination variations. Also, the obtained accuracy is good, but the computational complexity is high. Whereas co-occurrence matrix based statistical approach of object detection a good tradeoff between speed and accuracy. [ABSTRACT FROM AUTHOR]

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas. [ABSTRACT FROM AUTHOR]

In foggy days, the color saturation and contrast of the image are reduced due to factors such as scattering and refraction of light. Aiming at the problems of fog residue and loss of detailed features after processed by the existing dehazing methods, a defogging model network APSA-DehazeNet (Adaptive Pyramid Split Attention-DehazeNet) based on split convolution is proposed in this paper. Firstly, the adaptive multi-scale feature fusion module is used to extract the features of the foggy image, capture the features of different scales, then perform weighted fusion and obtain the shallow features of the image. Secondly, the deep features of the image are further obtained by the split convolutional network (PSANet) based on attention mechanism, and a more thorough dehazing effect is obtained. Finally, to effectively solve the problem of detail loss caused by a single loss, a joint loss function of perceived loss and structural similarity loss is proposed. Compared with other algorithms, experimental results demonstrate that the PSNR and SSIM indexes on the synthetic fog map dataset are improved by an average of 4.7dB and 7.4%, respectively. [ABSTRACT FROM AUTHOR]

Traditional classification methods for seismic fault 3D point cloud data rely on fault annotation data. Fault annotation data is usually stored in the data structure of a 3D array, and represented by organized point cloud data. The artificial fault annotation method analyses point data in each 2D slice respectively, without considering the 3D spatial distribution of all points, produces results without clustering and proper continuity in 3D space, and causes inconvenience for subsequent research work, such as calculation of the trend, inclination, and other information of each single fault. This paper presents a very simple but efficient clustering method for seismic fault annotation point cloud data to divide the points into each fault. To provide features as fundaments for this clustering method, we propose a normal direction estimation algorithm for seismic fault point cloud data. Tested by the experiments on synthetic data and field data, our method can divide the points with accuracy, reliability, and adaptability, thus providing a foundation for unified analysis, processing, and calculation for each part of the same fault, and analyzing fault displacement and low sequence faults, moreover, the clustering result could be used to fix 3D continuity of fault annotation data itself. Highlights: Directly analysis of organized point cloud to clustering faults. Proposes an efficient and reliable fault feature extraction and analysis algorithm. Directly analyze the original fault annotation 3D point cloud without approximation. [ABSTRACT FROM AUTHOR]

In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive compression method of massive data based on improved clustering algorithm is proposed in this paper. Through the micro clustering stage of birch method based on K-Medoids clustering, Clustering Feature Tree hierarchy is constructed and numerical clustering features are extracted; Taking this feature as the input of macro clustering order, the Clustering Feature Tree leaf nodes are clustered based on the improved K-Medoids clustering method, and the clustering data cluster set is output; The set is used as the original data of real-time progressive compression, and the data is denoised and compressed by lifting format wavelet transform. On this basis, Huffman coding is used to compress the data losslessly. The test results show that this method has good clustering effect under the optimal number of clustering centers, can complete the real-time progressive compression of a large number of data, and the availability of compressed data is more than 92%. [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]

Simultaneous Localization and Mapping (SLAM) technology is crucial for achieving spatial localization and autonomous navigation. Finding image features that are representative presents a key challenge in visual SLAM systems. The widely used ORB (Oriented FAST and Rotating BRIEF) algorithm achieves rapid image feature extraction. However, traditional ORB algorithms face issues such as dense, overlapping feature points, and imbalanced distribution, resulting in mismatches and redundancies. This paper introduces an image feature extraction algorithm called Adaptive Threshold and Local Gray Difference-ORB(ALGD-ORB) to address these limitations. Specifically, an adaptive threshold is employed to enhance feature point detection, and an improved quadtree method is used to homogenize feature point distribution. This method combines feature descriptors generated from both gray size and gray difference to enhance feature descriptor distinctiveness. By fusing these descriptors, their effectiveness is improved. Experimental results demonstrate that the ALGD-ORB algorithm significantly enhances the uniformity of feature point distribution compared to other algorithms, while maintaining accuracy and real-time performance. [ABSTRACT FROM AUTHOR]

To address the problem of unbalanced computational resource consumption and accuracy of semantic segmentation models using attention mechanism, this paper proposed a lightweight attention enhancement algorithm for semantic segmentation. Firstly, it designed a striped dimensional attention mechanism based on the shape characteristics of objects in driving scenes, used striped pooling instead of traditional square convolution, and combined dimensionality reduction operations to extract long-range semantic associations in each dimension to cut down the model computation. Then it fused the attention on channel domain and spatial domain to form a lightweight multidimensional attention fusion module that could be superimposed and disassembled to extract feature information in all directions and further improve the model accuracy. Finally, it inserted the module into the ResNet-101 backbone based encoding-decoding network to guide the semantic fusion of high and low layers, correct the feature map edge information, and supplement the prediction details. The experiments show that the module has strong robustness and generalization ability, cutting about 90% of the number of parameters and 80% of the computation compared with the same type of attention mechanism, and the segmentation accuracy still achieves a stable improvement. [ABSTRACT FROM AUTHOR]

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left–right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery [ABSTRACT FROM AUTHOR]

The available surface defect detection methods for disposable wooden spoons still involve screening with the naked eye. This detection method is not only inefficient but also accompanied by problems such as false detection and missed detection. Therefore, this paper proposes a detection method based on an improved YOLOv5 network model (YOLOv5-TSPP). This method uses the K-Means ++ algorithm to cluster the target samples in the data set to obtain anchor frames that are more in line with different target scales. The Coordinate Attention module is added to the backbone network of the YOLOv5 network model to improve the feature extraction ability of the model. A new SPP module is added to the backbone network to increase the important features in the receptive field extraction network to improve the detection accuracy of small targets. The experimental results show: The YOLOv5-TSPP algorithm has better detection performance and the mAP of defect detection reaches 80.3%, which is 9.2% higher than that of the YOLOv5 algorithm. Among them, the detection accuracy of black knot defect reached 98.6%, the detection accuracy of back crack defect reached 92.1%, and the detection accuracy of mineral line defect reached 92.3%. [ABSTRACT FROM AUTHOR]