改进YOLOv5的沥青路面裂缝检测方法
Asphalt Pavement Crack Detection Method Based on Improved YOLOv5
王莉静 1孙泽然 1李志猛 1丰吉科1
作者信息
- 1. 天津城建大学 控制与机械工程学院,天津 300384
- 折叠
摘要
针对YOLOv5 在裂缝图像目标检测中未能考虑到裂缝图像背景复杂,检测目标较小导致检测效果不佳和易出现误检漏检的问题,提出了一种改进YOLOv5 的沥青路面裂缝检测方法.该算法首先将轻量级Mobilenet v3 的网络作为YOLOv5 的特征提取骨干网络,以降低模型复杂度并加快推理速度.同时,在网络预测端引入高效通道注意力机制,提升网络局部特征捕获和融合能力.最后,通过一个嵌入Panet模块来强化裂缝图像的多尺度特征表达能力,提高对小目标的检测效果.实验结果表明,相比于原始YOLOv5 算法,改进后的YOLOv5 进行沥青路面裂缝检测的平均精度提高了5.6%,模型参数量降低了86.3%,图像检测时间减少了75.8%.
Abstract
A improved YOLOv5 asphalt pavement crack detection method is proposed to address the i-ssues of complex crack image backgrounds,small detection targets,poor detection performance,and missed detections in YOLOv5 crack detection.Firstly,the lightweight Mobilenet v3 network,as the fea-ture extraction network of YOLOv5,is used to reduce the complexity of the model and speed up reaso-ning.Secondly,an efficient channel attention mechanism(CBAM)is employed to enhance the net-work's ability to capture and fuse local features.Finally,an embedded Panet module is used to en-hance the multi-scale feature expression ability of crack images and improve the detection performance of small targets.The experimental results show that compared to the original YOLOv5 algorithm,the im-proved YOLOv5 algorithm improves the mAP of asphalt pavement crack detection by 5.5%,reduces the number of model parameters by 86.3%,and reduces image detection time by 75.8%.
关键词
YOLOv5/目标检测/沥青路面/裂缝检测Key words
YOLOv5/object detection/asphalt pavement/crack detection引用本文复制引用
基金项目
天津市自然科学基金(20YDTPJC00840)
天津城建大学研究生教育教学改革与研究重点项目(JG-ZD-2205)
出版年
2024
NETL
NSTL
万方数据