基于改进YOLO v5的骑行人员头盔及车牌检测

针对目前骑行人员头盔佩戴检测的准确率低、泛化能力差以及检测类别单一等问题，提出一种基于改进YOLO v5的骑行人员头盔及车牌检测模型。首先，在骨干网络中引入卷积注意力模块(convolutional block attention module, CBAM),以强化目标区域的关键特征，提高模型的准确率。其次，通过优化多尺度特征融合模块，并在预测端新增针对小目标特征的检测层，增强网络在密集场景下对小目标的检出率，提升模型的泛化能力。最后，使用EIoU(efficient intersection over union)优化边框回归，同时采用K-means算法在创建的头盔及车牌数据集中聚类先验框，以加速模型训练的收敛速度并提高目标定位的精度。实验结果表明，改进后的YOLO v5网络的检测准确率提高2.5%,召回率提高3.3%,平均精度均值提高3.8%,更适用于对骑行人员头盔及车牌目标的检测。

Helmet and license plate detection for cyclists based on improved YOLO v5

An improved YOLO v5 model for cyclist helmet and license plate detection is proposed to solve the problems of low accuracy,poor generalization ability and single detection categories in helmet detection.Firstly,the convolutional block attention module (CBAM) is introduced into the backbone network to strengthen the key features of the target region and improve the accuracy of the model. Secondly,by optimizing the multi-scale feature module and adding a detection layer for tiny targets in the prediction end,the detection rate of the network for small targets in dense scenes is enhanced,and the generalization ability of the model is improved.Finally,the model training convergence speed is accelerated and target localization accuracy is improved by optimizing the bounding box regression using efficient intersection over union (EIoU) and by clustering new anchor box sizes using the K-means algorithm in the helmet and license plate dataset created .The experimental results show that the improved YOLO v5 model has achieved an increase in detection accuracy rate of 2.5%,a recall rate increase of 3.3%,and an average precision increase of 3.8%,which makes it more suitable for detecting helmet and license plate targets of cyclists.