基于改进Yolov5s的路面裂缝病害检测与识别研究  

Research on Detection and Identification of Pavement Crack Diseases Based on Improved Yolov5s

作　　者：陈修贤 高焕兵[1,2] 杨志强 孔滕广[1,2] 车仁海 CHEN Xiuxian;GAO Huanbing;YANG Zhiqiang;KONG Tengguang;CHE Renhai(School of Information and Electrical Engineering,Shandong Jianzhu University;Shandong Key Laboratory of Intelligent Building Technology,Ji′nan 250101,China;Shandong Quanhai Automotive Technology Co.,Ltd,Ji′nan 252899,China)

机构地区：[1]山东建筑大学信息与电气工程学院 [2]山东建筑大学山东省智能建筑技术重点实验室,山东济南250101 [3]山东泉海汽车科技有限公司,山东济南252899

出　　处：《软件导刊》2024年第12期206-212,共7页Software Guide

基　　金：国家自然科学基金项目(61903227);山东省自然科学基金项目(ZR2022MF267)。

摘　　要：公路路面裂缝是沥青路面病害中的重要影响因素,而路面裂缝检测是路面养护的重要一环。针对公路路面裂缝检测算法存在漏检误检和识别精确度低的问题,提出一种基于改进Yolov5s的路面裂缝病害检测模型。首先,采取注意力机制模块CBAM学习目标特征和位置特征并增加有用特征权重;其次,提出Decoupled解耦头方法将特征图通过不同分支进行分开处理以此提升训练精度;最后,提出改进后的αDIoU损失函数替换原始模型中的CIoU损失函数,并选用α=3来提升high IoU object的loss梯度值及框的回归效果。实验表明,改进后模型平均检测精确度为92.8%,召回率为94.5%,mAP值为96.5%,相较于原始模型提升了1.8%,证明了改进后模型在检测精度上具有较高的提升效果,能满足对于公路路面裂缝的识别检测任务。Highway pavement cracks are an important influencing factor in asphalt pavement diseases,and pavement crack detection is an important part of pavement maintenance.A pavement crack disease detection model based on improved Yolov5s is proposed to address the problems of missed detections,false detections,and low recognition accuracy in detection algorithms for highway pavement cracks.Firstly,the attention mechanism module CBAM is adopted to learn target features and positional features,and to increase useful feature weights;Secondly,the Decoupled decoupling head method is proposed to separate the feature maps through different branches for processing,in order to improve training accuracy;Finally,an improvedαDIoU loss function is proposed to replace the CIoU loss function in the original model,andα=3 is selected to enhance the loss gradient value of the high IoU object and the regression effect of the box.The experiment shows that the improved model has an average detection accuracy of 92.8%,a recall rate of 94.5%,and an mAP value of 96.5%,which is 1.8%higher than the original model.This proves that the improved model has a high improvement effect on detection accuracy and can meet the recognition and detection tasks of highway pavement cracks.

关 键 词：路面裂缝检测 Yolov5s 注意力机制 解耦头 损失函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]