基于改进YOLOv7的码头作业人员检测算法OA北大核心CSTPCD
An Improved YOLOv7 Algorithm for Workers Detection in Port Terminals
广角监控图像中人员目标检测对于码头智能安防具有重要意义.针对传统YOLOv7算法在码头广角监控图像识别中,存在小目标特征提取能力弱、人员检测准确率低等问题,研究了基于改进YOLOv7的码头作业人员检测算法.为提升人员目标多尺度特征的检测性能及鲁棒性,设计了平衡码头人员分类与定位任务的上下文解耦(task-specific context decoupling,TSCODE)结构并联合聚集-分发机制(gather-and-distribute,GD),增强网络多尺度特征融合能力;为增强网络对作业人员等小目标的特征提取能力,在主干网络末端引入了基于双层路由注意力机制(bi-level routing attention,BRA)的视觉transformer模型(BRA-ViT),捕捉小目标人员的位置、方向与跨通道等信息;为提升检测速度并保持检测精度,提出了基于slim-neck的颈部层网络轻量化方法,降低参数量与计算量;为降低漏检率与误检率,引入了基于最小点距离的交并比损失函数(mini-mum-point-distance-based intersection over union,MPDIoU)计算边界框的坐标预测损失,提升边界框回归的准确性与计算效率.为验证算法效果,采集白天、夜晚不同时段下码头前沿、堆场、卡口等场景的广角监控图像,构造标注数据集并设计消融与对比实验.实验结果显示:所提算法对码头作业人员检测的平均准确率为90.6%,平均检测速度为39 fps;与Faster R-CNN、SSD、YOLOv3、YOLOv5、YOLOv7、YOLOv8等算法相比,其平均准确率分别提升了13.8%、15.8%、8.5%、5.2%、2.7%和3.5%,平均检测速度与基准YOLOv7算法性能相当.所提算法对码头作业人员识别具有较高的检测精度与检测速度,满足码头安防场景中作业人员检测准确性与实时性的要求.
Accurate detection of workers in wide-angle surveillance images is significant for intelligent surveillance in port terminals.However,the traditional YOLOv7 algorithm has limitations on the recognition of workers in wide-angle surveillance images,such as weak feature extraction ability,low detection accuracy,etc.To fill these gaps,an algorithm for terminal worker detection based on improved YOLOv7 is proposed.A task-specific context decoupling(TSCODE)structure balancing the classification and localization tasks is designed,and the gath-er-and-distribute mechanism(GD)improving the fusion of multi-scale features is applied,which improves the per-formance and robustness of multiscale features detection from various workers'images.To strengthen the feature extraction of small targets,the vision transformer with bi-level routing attention(BRA-ViT)is introduced into the end of the backbone network,capturing the position,direction,and cross-channel information of small objects.The slim-neck is used to lighten the neck of the network,refine the number of parameters,and reduce computational complexity,enhancing detection speed while maintaining detection accuracy.Fourthly,a loss function with mini-mum-point-distance-based intersection over union(MPDIoU)is used to calculate the prediction loss of the bound-ing box,reducing the rates of false negatives and false positives.To validate the proposed algorithm,wide-angle sur-veillance images in different areas of the port(quay,yard,chokepoint,and other locations)at different times(day and night)are collected and annotated in the dataset,and ablation and comparison experiments are implemented.The results show that the average detection precision(AP)and average detection speed of the proposed algorithm are 90.6%and 39 fps,respectively.Compared with Faster R-CNN,SSD,YOLOv3,YOLOv5,YOLOv7,and YO-LOv8,AP of the proposed algorithm is improved by 13.8%,15.8%,8.5%,5.2%,2.7%,and 3.5%,respectively;FPS of the proposed algorithm is similar to the baseline YOLOv7 algorithm.In summary,the proposed algorithm has higher AP than existing algorithms with responsible detection speed,which is suitable for real-time safety and secu-rity surveillance in port terminals.
张孝杰;张艳伟;邹鹰;尹学成;程祈文;沈汝超
武汉理工大学交通与物流工程学院 武汉 430063上海国际港务(集团)股份有限公司 上海 200080连云港新圩港码头有限公司 江苏 连云港 222248
计算机与自动化
交通安全广角监控图像码头作业人员检测定位YOLOv7
transport safetywide-angle surveillance imagesterminal's workers detection and localizationYO-LOv7
《交通信息与安全》 2024 (002)
67-75 / 9
国家科技重大专项项目(2022ZD0119303)资助
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