Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dens...Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.展开更多
Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unman...Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.展开更多
In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety ris...In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.展开更多
To address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision,this study developed an enhanced YOLOv8n-SS ...To address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision,this study developed an enhanced YOLOv8n-SS pedestrian detection algorithm through architectural modifications to the baseline YOLOv8n framework.The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions,including variable lighting and mechanical occlusions.The main innovations were:(1)integration of spatial pyramid dilated(SPD)operations with conventional convolution layers to construct SPD-Conv modules,which effectively mitigated feature information loss while enhancing small-target detection accuracy;(2)incorporation of selective kernel attention mechanisms to enable context-aware feature selection and adaptive feature extraction.Experimental validation revealed significant performance improvements over the original YOLOv8n model.This enhanced architecture achieved 7.2% and 9.2% increases in m AP0.5 and m AP0.5:0.95 metrics respectively for dense pedestrian detection,with corresponding improvements of 7.6% and 8.7% observed in actual farmland working environments.The proposed method ultimately provides a computationally efficient and robust intelligent monitoring solution for agricultural mechanization,facilitating the transition from conventional agricultural practices toward sustainable,low-carbon production paradigms through algorithmic optimization.展开更多
基金supported in part by the National Science Foundation of China(52371372)the Project of Science and Technology Commission of Shanghai Municipality,China(22JC1401400,21190780300)the 111 Project,China(D18003)
文摘Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.
基金This research was funded by the Natural Science Foundation of Hebei Province(F2021506004).
文摘Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.
文摘In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.
基金supported by the General Program of the Natural Science Foundation of Hunan Province of China(2021JJ30359)。
文摘To address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision,this study developed an enhanced YOLOv8n-SS pedestrian detection algorithm through architectural modifications to the baseline YOLOv8n framework.The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions,including variable lighting and mechanical occlusions.The main innovations were:(1)integration of spatial pyramid dilated(SPD)operations with conventional convolution layers to construct SPD-Conv modules,which effectively mitigated feature information loss while enhancing small-target detection accuracy;(2)incorporation of selective kernel attention mechanisms to enable context-aware feature selection and adaptive feature extraction.Experimental validation revealed significant performance improvements over the original YOLOv8n model.This enhanced architecture achieved 7.2% and 9.2% increases in m AP0.5 and m AP0.5:0.95 metrics respectively for dense pedestrian detection,with corresponding improvements of 7.6% and 8.7% observed in actual farmland working environments.The proposed method ultimately provides a computationally efficient and robust intelligent monitoring solution for agricultural mechanization,facilitating the transition from conventional agricultural practices toward sustainable,low-carbon production paradigms through algorithmic optimization.
