改进YOLOv5的沥青路面裂缝检测方法

Asphalt Pavement Crack Detection Method Based on Improved YOLOv5

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%。

A improved YOLOv5 asphalt pavement crack detection method is proposed to address the issues 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 feature extraction network of YOLOv5,is used to reduce the complexity of the model and speed up reasoning.Secondly,an efficient channel attention mechanism(CBAM)is employed to enhance the network’s ability to capture and fuse local features.Finally,an embedded Panet module is used to enhance 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 improved 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%.