改进YOLOv5s算法的电动车头盔检测研究  

Improved YOLOv5s algorithm for electric bike helmet wearing detection

作　　者：侯恩翔 张旭 刘罡 张秀再[1] Hou Enxiang;Zhang Xu;Liu Gang;Zhang Xiuzai(School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Electronics and Information Engineering,Wuxi University,Wuxi 214105,China)

机构地区：[1]南京信息工程大学电子信息工程学院,南京210044 [2]无锡学院电子信息工程学院,无锡214105

出　　处：《国外电子测量技术》2024年第3期168-176,共9页Foreign Electronic Measurement Technology

基　　金：国家自然科学基金(62204172)项目资助。

摘　　要：针对电动车头盔佩戴检测存在遮挡、车辆密集以及一车多人的复杂场景下出现的漏检、误检问题,在YOLOv5s的基础上,提出了一种应用于电动车头盔佩戴检测的改进算法。设计了一种由递归门控卷积改进的GBC3模块,替换网络主干和特征融合层(feature pyramid networks,FPN)中的C3模块,加强邻间特征的空间信息交互,提高网络的特征提取和特征融合能力;其次在主干和特征融合网络添加无参注意力机制(SimAM),以调整特征图中不同区域的注意力权重,对重要目标施加更多关注;最后引入调整超参后的WIOU损失函数,优化预测框回归,提高模型的目标定位能力。在自制电动车头盔数据集上的实验结果表明,改进模型在仅增加较少参数的前提下,其平均精度均值(mAP)达到97.3%,较YOLOv5s提高了3.2%,并且检测速度为87.1 fps,改善了误检和漏检的问题,同时仍具有较高的实时性,更适用于电动车驾乘者的头盔佩戴检测。Aiming at the leakage and misdetection problems of electric vehicle helmet wearing detection in the presence of occlusion,dense vehicles and the complex scene of multiple people in one vehicle,an improved algorithm applied to electric vehicle helmet wearing detection is proposed on the basis of YOLOv5s.A GBC3 module improved by recursive gating convolution is designed to replace the C3 module in the network backbone and feature fusion layer(feature pyramid networks,FPN),strengthen the spatial information interaction of neighbor features,and improve the feature extraction and feature fusion capabilities of the network.Secondly,non-parametric attention mechanism(a simple,parameter-free attention module for convolutional neural networks,SimAM)is added to the backbone and feature fusion network to adjust the attention weight of different regions in the feature map and pay more attention to important targets.Finally,the WIOU loss function is introduced to optimize the prediction box regression and improve the target localization ability of the model.The experimental results on the self-made electric vehicle helmet dataset show that the mAP of the improved model reaches 97.3%under the premise of only adding fewer parameters,which is 3.2%points higher than that of YOLOv5s,and the detection speed is 87.1 fps,which improves the problem of false detection and missed detection,and still has high real-time performance,which is more suitable for helmet wearing detection of electric vehicle drivers.

关 键 词：电动车头盔检测 递归门控卷积 空间交互 注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]