基于改进YOLOv5的路面坑洼检测方法

Pavement Pothole Detection Method Based on Improved YOLOv5

坑洼是一种常见的路面病害,会降低行车安全,准确快速地检测路面坑洼较为重要。针对现有坑洼检测方法在小目标和密集目标的场景下检测精度不高的问题,文中提出了一种改进YOLOv5(You Only Look Once version 5)模型。在YOLOv5的主干网络中引入CBAM(Convolutional Block Attention Module)来提高模型对关键特征的注意能力,将YOLOv5的损失函数改为EIoU(Efficient Intersection over Union)来提高模型对目标的检测精度。实验结果表明,所提模型能够在小目标和密集目标的场景下快速准确地检测路面坑洼,在开源数据集Annotated Potholes Image Dataset中的mAP(mean Average Precision)达到了82%,较较于YOLOv5和其他主流方法也有所提高。

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 network 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.