现代信息科技2025，Vol.9Issue(3)：68-72,78,6.DOI:10.19850/j.cnki.2096-4706.2025.03.013
基于YOLOv5的井盖隐患智能识别研究
Research on Intelligent Recognition of Hidden Dangers of Manhole Covers Based on YOLOv5
摘要
Abstract
As a key protective component of the urban underground pipe network system,the safety status of manhole covers directly affects the operation and maintenance efficiency and public safety of municipal facilities.To address the inefficiency and high false alarm rate of traditional methods for detecting hidden dangers in manhole covers,this paper proposes an intelligent detection method based on improved YOLOv5.By constructing a multi-scale feature fusion mechanism,this paper combines the 5-fold cross-validation method to train the model of the labeled dataset,and realizes the accurate identification of typical safety hazards such as breakage,displacement,loss,and so on.The experimental results show that the mean Average Precision(mAP)of the improved model on the self-constructed manhole cover dataset is 95.2%.Compared with the YOLOv4 model,the accuracy and detection speed are improved.By optimizing the Feature Pyramid Network structure and loss function,the algorithm effectively enhances the target representation ability in complex road scenarios,providing reliable technical support for intelligent operation and maintenance of urban infrastructure.关键词
井盖隐患/YOLOv5/目标检测/激活函数Key words
hidden danger of manhole cover/YOLOv5/object detection/activation function分类
信息技术与安全科学引用本文复制引用
黄健,向思怡..基于YOLOv5的井盖隐患智能识别研究[J].现代信息科技,2025,9(3):68-72,78,6.基金项目
陕西省大学生创新创业计划项目(S202412715035) （S202412715035）
