摘要
针对现有路面裂缝检测精度低、定位能力差和计算量较大等问题,对基于YOLO v8n的轻量化检测模型进行研究;采用GhostNet v2作为主干网络以减少参数量并提升特征提取性能,在特征融合模块引入CBAM增强对路面缺陷特征的关注,将边界框回归损失函数替换为Focal-SIoU以优化边界框重叠度计算;实验结果显示,改进后模型的精确率、召回率和全类平均正确率分别提升4.76%、1.89%和2.77%;模型大小为YOLO v8n的33.33%,推理速度达271.5 frames·s^(-1),检测1000张图片仅需3.68 s;该模型满足实时性要求,可减少路面裂缝漏检和误检,为路面移动端设备自动检测提供技术支持。
Aiming at the problems of low accuracy,poor positioning ability,and large computational load in existing pavement crack detection,this study investigates a lightweight detection model based on YOLO v8n.GhostNet v2 is adopted as the backbone network to reduce the number of parameters and improve feature extraction performance.The CBAM(Convolutional Block Attention Module)is introduced into the feature fusion module to enhance attention to pavement defect features,and the bounding box regression loss function is replaced with Focal-SIoU to optimize the calculation of bounding box overlap.Experimental results show that the precision,recall,and mean average precision of the improved model are increased by 4.76%,1.89%,and 2.77%,respectively.The model size is 33.33%of that of YOLO v8n,with an inference speed of 271.5 frames•s^(-1),and it takes only 3.68 seconds to detect 1000 images.The model meets real-time requirements,reduces missed and false detections of pavement cracks,and provides technical support for automatic detection on mobile pavement devices.
作者
罗瑜
樊赫
LUO Yu;FAN He(School of Electrical Engineering,Shaanxi Polytechnic University,Xianyang 71200o,China;Northwest Institute of Mechanical and Electrical Engineering,Xianyang 712099,China)
出处
《计算机测量与控制》
2026年第2期65-72,86,共9页
Computer Measurement & Control
基金
陕西工业职业技术大学自然科学类科研项目(2024YKYB-001)。
