改进YOLOv5s的路面坑槽目标检测模型  

Model of pavement pothole target detection with improved YOLOv5s

作　　者：赵江平[1] 王欣然 吴立舟 ZHAO Jiangping;WANG Xinran;WU Lizhou(School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055

出　　处：《中国安全科学学报》2025年第1期67-74,共8页China Safety Science Journal

摘　　要：为提高道路安全巡检工作中路面坑槽隐患的检测效率和自动化水平,降低交通事故发生概率,构建一种基于改进YOLOv5s的路面坑槽隐患智能检测模型。在原YOLOv5s网络中加入自适应空间特征融合(ASFF)模块,将主干网络替换为FasterNet网络,引入轻量通道注意力(ECA)模块;通过消融试验分析改进模块对检测模型性能的影响,验证目标检测效果,并开发交互式可视化检测界面。结果表明:改进后的模型精度、召回率和平均检测精度分别提升了4.1%、9.9%和5.6%。较原网络有较为显著的提升,具有良好的检测效果,能够满足路面坑槽自动化检测的应用需求,提高巡检效率,减少因路面坑槽导致的交通事故。To improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work,reduce the probability of traffic accidents.A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed.This method incorporated the ASFF module into the original YOLOv5s network,replaced the backbone network with the FasterNet network,and further introduced the Efficient Channel Attention(ECA)module.Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model,to verify the target detection effect,and to develop an interactive visualized detection interface.The results show that the improved model accuracy,recall rate,and average detection accuracy have increased by 4.1%,9.9%and 5.6%respectively.Compared to the original network,the improvement is significant.It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves,thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.

关 键 词：YOLOv5s 路面坑槽 目标检测 自适应空间特征融合(ASFF) FasterNet 

分 类 号：X924.3[环境科学与工程—安全科学]