2024 03 v.39;No.122 61-72
基于改进YOLOv7的防护用品穿戴检测
基金项目(Foundation):
邮箱(Email):
DOI:
中文作者单位:
广东潮州卫生健康职业学院;
摘要(Abstract):
为缓解YOLOv7在检测个人防护用品时面临标签重写、标签分配不平衡和特征耦合等问题,提出一种基于改进YOLOv7的检测方法.首先去除YOLOv7的大尺度和中尺度输出层,以降低标签重写率,且保证输出层得到充分训练;其次将输出层的定位和分类解耦,避免不同任务的特征表示互相影响,并选择在边界框级别检测防护服,在关键点级别检测防护帽和防护手套;最后引入部分卷积,实现实时检测.为验证该方法的有效性,使用实验人员穿戴防护用品的图像数据对所提方法进行验证.结果表明,相比YOLOv7,该方法的精确率和召回率分别提高了4.1和4.5个百分点,FPS(Frames Per Second)提升了1.3帧,可满足实验室场景下的个人防护用品穿戴检测需求.
关键词(KeyWords):
个人防护用品;;穿戴检测;;YOLOv7;;单尺度输出;;特征解耦;;部分卷积
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参考文献
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[12] WANG C Y,BOCHKOVSKIY A,LIAO H Y M. YOLOv7:Trainable bag-of-freebies sets new stateof-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2023:7464-7475.
[13] REN S,HE K,GIRSHICK R,et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,2017,39(6):1137-1149.
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基本信息:
DOI:
中图分类号:TP391.41
引用信息:
[1]杨晓帆,韦少钗.基于改进YOLOv7的防护用品穿戴检测[J].汕头大学学报(自然科学版),2024,39(03):61-72.
基金信息:
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