基于数字图像的砌体涵洞病害识别与工程应用  

Identification and Engineering Application of Masonry Culvert Disease Based on Digital Image

作　　者：蒋买勇[1,2] 庄泽麟 谭文波 李金友 JIANG Mai-yong;ZHUANG Ze-in;TAN Wen-bo;LI Jin-you(Hunan Polytechnic of Water Resources and Electric Power,Changsha 410131,Hunan Province,China;School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,Hubei Province,China)

机构地区：[1]湖南水利水电职业技术学院,湖南长沙410131 [2]武汉大学水利水电学院,湖北武汉430072

出　　处：《中国农村水利水电》2023年第8期265-271,287,共8页China Rural Water and Hydropower

基　　金：湖南省水利科技重大项目(XSKJ2018179-01,XSKJ2022068-07);湖南省水利科技项目(XSKJ2019081-20,XSKJ2019081-37);湖南省自然科学基金资助项目(2019JJ70053)。

摘　　要：随着砌体水工涵洞服役年限的增长,现有涵洞结构亟待得到快速准确的检测与评估诊断。基于数字图像的检测方法可以显著提高检测效率,但受限于水工涵洞病害检测时光照条件恶劣、空间狭小等因素,给该方法的应用带来诸多挑战。针对上述问题,建立基于YOLOv5网络的水工涵洞病害检测模型,通过对采集的涵洞图像进行目标检测,并将识别结果进行优化,实现对水工涵洞病害的快速识别。运用Mosaic数据增强、多尺度训练及Adam优化器对模型的性能进行优化分析,以提高模型的重建效果。现场实例应用结果表明,基于YOLOv5网络能够快速、高效地识别水工涵洞病害,该方法可克服水工涵洞的外观干扰特征,其识别精度与实际情况相符,可准确判别水工涵洞病害等级,具有广泛的应用前景。With the increase in the service life of masonry hydraulic culvert,the existing culvert structure needs to be quickly and accurately detected and evaluated.The detection method based on digital image can significantly improve the detection efficiency,but the application of this method is faced with many challenges due to the poor lighting conditions and narrow space in the detection of hydraulic culvert diseases.In view of the above problems,a hydraulic culvert disease detection model based on YOLOv5 network is established.Through the target detection of the collected culvert images and optimization of the recognition results,the rapid identification of hydraulic culvert diseases is realized.Mosaic data enhancement,multi-scale training and Adam optimizer are used to optimize and analyze the performance of the model to improve the reconstruction effect of the model.The application results of field examples show that the YOLOv5 network can quickly and efficiently identify hydraulic culvert diseases.This method can overcome the appearance interference characteristics of hydraulic culvert,and its recognition accuracy is consistent with the actual situation.It can accurately identify the disease grade of hydraulic culvert,and has a broad application prospect.

关 键 词：水工涵洞 病害识别 目标检测 神经网络 YOLOv5网络 

分 类 号：TV223.4[水利工程—水工结构工程]