基于改进YOLOv5的路面坑洼检测方法OA

Pothole is a common road disease,it reduce driving safety,accurate and rapid detection of potholes is more important.In viewof the problem that the detection accuracy of existing pothole detection methods is not high in the scenario of small targets and dense targets,an improved YOLOv5(You Only Look Once version 5)model is proposed in this study.TheCBAM(Convolutional Block Attention Module)is introduced into YOLOv5's backbone net-work to improve the model's ability to pay attention to key features.The loss function of YOLOv5 is changed to EIoU(Efficient Intersection over Union)to improve the detection accuracy of the model.The experimental results show that the proposed model can detect Potholes quickly and accurately in the scenarios of small targets and dense targets,and the mAP(mean Average Precision)in the open source Annotated Potholes Image Dataset reaches 82%.Compared with YOLOv5 and other mainstream methods,it is also improved.