轻量化YOLOv5s网络车底危险物识别算法

Lightweight YOLOv5s network-based algorithm for identifying hazardous objects under vehicles

针对现有车底危险物检测模型结构复杂、参数量大、不易部署于端侧的问题,提出轻量化SG-YOLOv5s网络模型.对YOLOv5s网络的骨干和颈部进行优化改进,显著降低网络的参数量,大幅缩小模型的权重体积;在训练阶段采用Mixup数据增强,提高模型的泛化能力;采用SIoU替换边框回归损失函数CIoU,使危险物预测框更接近真实框,提高检测精度.鉴于车底危险物数据集较少的现状,利用智能小车拍摄大量车底碎片化图像,采用AutoStitch算法进行图像拼接,最终获得自建车底图像数据集.实验结果表明:在自建的9种模拟车底危险物数据集上,SG-YOLOv5s模型识别精确率为97.63%,相较于原YOLOv5s模型提升了1.26%,而参数量减少了71.27%,模型权重体积下降了71.28%,为后续识别模型的嵌入式部署提供了可能.

A lightweight SG-YOLOv5s network model was proposed to solve the problems of complex structure,large number of parameters and difficult deployment on the end side of the current detection model of hazardous objects under vehicle.Firstly,the backbone and the neck of YOLOv5s network were optimized and improved,which significantly reduced the number of network parameters and greatly reduced the weight volume of the model.Secondly,in the training stage,Mixup data was used to enhance the generalization ability of the model.Finally,SIoU was used to replace the frame regression loss function CIoU,so that the hazardous object prediction box was closer to the real box and the detection accuracy was improved.In view of the fact that there were few data sets of hazardous objects under vehicle,a large number of fragmented images of car undersides were captured using an intelligent small car,and the AutoStitch algorithm was adopted to splice the images,and finally the self-built data set of car bottom images was obtained.Experimental results show that,the identification accuracy rate of SG-YOLOv5s model was 97.63%in the self-built data sets of nine simulated vehicle hazards,which was 1.26%higher than that of the original YOLOv5s model.Additionally,the SG-YOLOv5s model reduced the number of parameters by 71.27%and decreased the model weight volume by 71.28%.These advancements provide the potential for embedded deployment of subsequent recognition models.