现代电子技术2025，Vol.48Issue(16)：7-12,6.DOI:10.16652/j.issn.1004-373x.2025.16.002
基于YOLOv5的无人机桥面病害检测算法研究
Research on UAV bridge deck disease detection method based on YOLOv5
戴鹏飞 1邹京汕 2杨柳 2刘恒 2阴慧颖3
作者信息
- 1. 南京工业大学,江苏 南京 211816||中铁桥隧技术有限公司,江苏 南京 210000
- 2. 西南交通大学 信息科学与技术学院,四川 成都 611756||西南交通大学 综合交通大数据应用技术国家工程实验室,四川 成都 611756
- 3. 西南交通大学 综合交通大数据应用技术国家工程实验室,四川 成都 611756||西南交通大学 唐山研究院,河北 唐山 063000
- 折叠
摘要
Abstract
Bridge diseases,such as concrete peeling,bridge cracks,rivet corrosion,etc.,mostly occur in local areas,but most of the bridge diseases are not located in the whole bridge at present.The full-field rapid location and detection of bridge deck diseases can be realized by combining the high-definition camera function of unmanned aerial vehicle(UAV)with the real-time target detection ability of YOLOv5.Therefore,an UAV bridge deck disease detection algorithm based on YOLOv5 is proposed.The UAV is used to collect data on the bridge pavement,and the lightweight model YOLOv5s is used as the basic detection model.The YOLOv5s model is improved as follows:two scales are added on the basis of the existing three characteristic maps detection with different scales to improve the detection accuracy of larger targets and smaller targets;Soft-NMS algorithm is used to instead of NMS algorithm.In order to ensure the full-field rapid positioning and detection accuracy of dense diseases,the collected bridge pavement data is input into the improved YOLOv5s model,and the output of the model is the detection result of bridge deck diseases.The experimental results show that the value of mAP@0.5 of the optimized YOLOv5s model can reach 92.0%,and the value of mAP@0.5:0.95 also can reach 73.2%.The processing speed of the model can reach 134 f/s,which effectively and accurately identifies bridge pavement diseases,and significantly improves the accuracy and efficiency of detection.关键词
桥面病害检测/YOLOv5/无人机/图像采集/多尺度检测/特征融合Key words
bridge deck disease detection/YOLOv5/unmanned aerial vehicle/image collection/multi-scale detection/feature fusion分类
信息技术与安全科学引用本文复制引用
戴鹏飞,邹京汕,杨柳,刘恒,阴慧颖..基于YOLOv5的无人机桥面病害检测算法研究[J].现代电子技术,2025,48(16):7-12,6.
