基于YOLOv5的钢桥面多类别病害检测方法
A Method for Detecting Multiple Category Diseases of Steel Bridge Deck Based on YOLOv5
吕惠 1王民 1彭祝涛 1尚飞1
作者信息
- 1. 重庆市智翔铺道技术工程有限公司 重庆 400074
- 折叠
摘要
针对钢桥面检测中出现的病害种类繁多、形状多样等问题,对照路面病害检测评定标准,需找到并训练一种适合实际钢桥面养护作业的钢桥面病害识别算法,满足全面自动化检测钢桥面病害的应用要求.文中通过多功能检测车采集钢桥面图像,并利用 labelimg 标注 9 种病害建立自制数据集,采用 YOLOv5 算法完成检测模型训练.结果表明,YOLOv5 模型的病害检测精确率可达到 80%,其中,对于裂缝类病害的检测精度相对较低,块状病害的检测精度较高.
Abstract
Due to the wide variety of types and shapes of defects in steel bridge deck detection,as well as the higher requirements compared to pavement disease detection and evaluation standards,it is nec-essary to find and train a steel bridge deck disease recognition algorithm suitable for practical steel bridge deck maintenance operations to meet the application requirements for fully automated detection of steel bridge deck defects.The multi-function detection vehicle is used to collect images of the steel bridge deck,and nine types of defects are labeled using labelimg to establish a self-made dataset.The YOLOv5 algorithm is used to complete the training of the detection model.The experimental re-sults show that the disease detection accuracy of the YOLOv5 model can reach 80%,with relatively low detection accuracy for crack-type defects and high detection accuracy for block-type defects.
关键词
钢桥面病害/目标检测/YOLOv5/裂缝Key words
steel bridge deck diseases/object detection/YOLOv5/crack引用本文复制引用
出版年
2024
NETL
NSTL
万方数据