改进YOLOv5s的桥梁表观病害检测方法
Apparent disease detection of bridges using improved YOLOv5s
董绍江 1谭浩 1刘超 1胡小林2
作者信息
- 1. 重庆交通大学机电与车辆工程学院,重庆 400074
- 2. 重庆工业大数据创新中心有限公司,重庆 404100
- 折叠
摘要
针对已有目标检测方法在混凝土桥梁表观病害检测的应用中识别精度低且伴随较多误检和漏检的问题,提出了一种改进的YOLOv5s桥梁表观病害检测方法.针对目前桥梁表观病害特征成分较复杂的问题,为了更有效地利用不同尺度的缺陷特征,在主干网中添加修改后的空间金字塔池化模块,提高了整体网络对缺陷特征信息的获取能力,同时减少了运算工作量;针对由病害图像中不同缺陷特征交叉分布导致的误检率、漏检率高的问题,在YOLOv5s网络中加入轻量化注意力模块;针对桥梁缺陷尺寸差异大、分类困难和数据集小而导致的边界回归不匹配的问题,采用考虑了向量角度的损失函数.实验证明,改进后的YOLOv5s检测器在桥梁表观病害目标检测识别任务中能够有效提高精度、降低误检率和漏检率.
Abstract
To solve the problems of low accuracy,high false detection rate,and high missed detection rate in current target detection methods for apparent diseases in concrete bridges,an improved YOLOv5s method is proposed. To achieve more effective fusion of features at different scales and increase receptive fields,an improved spatial pyramid pooling module is added to the YOLOv5s network to enhance feature extraction capabilities and reduce computational cost;a light-weight attention module is incorporated into the YOLOv5s network to tackle the high false detection and missed detection rates caused by the cross-distribution of different defect features in disease images;and a loss function considering vector angles is adopted to solve the problems related to varying defect sizes,classification difficulties and small dataset-induced boundary box regression mismatches. Experimental results show that the improved YOLOv5s detector significantly improves accuracy while reducing false detection and missed detection rates in the task of detecting apparent diseases in bridges.
关键词
病害检测/YOLOv5s/特征融合/平均精度Key words
disease detection/YOLOv5s/feature fusion/mean average accuracy引用本文复制引用
基金项目
国家自然科学基金资助项目(51775072)
重庆市科技创新领军人才支持计划项目(CSTCCCXLJRC201920)
重庆市高校创新研究群体(CXQT20019)
出版年
2024
国家科技期刊平台
NSTL
万方数据