电子科技2024，Vol.37Issue(7)：53-59,7.DOI:10.16180/j.cnki.issn1007-7820.2024.07.007
基于改进YOLOv5的路面坑洼检测方法
Pavement Pothole Detection Method Based on Improved YOLOv5
摘要
Abstract
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.关键词
路面坑洼/深度学习/YOLOv5/注意力机制/CBAM注意力/小目标检测/密集目标检测/损失函数Key words
pavement potholes/deep learning/YOLOv5/attention mechanism/CBAM attention/small target detection/dense target detection/loss function分类
信息技术与安全科学引用本文复制引用
何幸,黄永明,朱勇..基于改进YOLOv5的路面坑洼检测方法[J].电子科技,2024,37(7):53-59,7.基金项目
江苏省重点研发计划(BE2020116)Jiangsu Provincial Key R&D Programme(BE2020116) （BE2020116）
