摘要
为了实现盾构隧道衬砌表面渗水、裂缝、掉块、漏泥砂等病害的快速准确识别,提出一种基于深度学习模块化设计的盾构隧道衬砌多类表观病害检测模型。该模型分为数据加载、网络结构、损失函数与后处理、训练与评估4个模块,结合SSD(Single Shot MultiBox Detector)与YOLOv4(You Only Look Once)的检测原理和数据集特点,提出采用适应度和最大可能召回率两个指标来综合评估模型先验框与数据集的匹配度。根据数据集病害标注框分布,采用K-means方法聚类得到匹配度最高的一组先验框,并考虑YOLOv4模型结构特点对SSD模型结构进行优化。结果表明,优化后的模型检测准确度达到0.623,相较于原SSD模型的0.373提高了近70%,检测速度由40 FPS提升至50 FPS,充分证明了优化模型的合理性。
In this paper,a detection model for many apparent defects of shield tunnel lining based on deep learning modular design is proposed to quickly and accurately identify defects such as water seepage,cracking,falling blocks,and mud&sand leaking on the surface of shield tunnel lining.The model is divided into four modules:data loading,network structure,loss function and post-processing,training and evaluation.Combining the detection principle and data set characteristics of SSD(Single Shot MultiBox Detector)and YOOv4(You Only Look Once),it is proposed to comprehensively evaluate the matching degree between the prior boxes of the model and this dataset by using two indicators:fitness and maximum possible recall rate.Based on the distribution of defect labeling boxes of the dataset,the K-means method is used to cluster and obtain a set of prior boxes with the highest matching degree.The structure of the SSD model is optimized by considering the structural characteristics of the YOLOv4 model.The results show that the optimized model has a detection accuracy of 0.623,which is nearly 70%higher than that of the original SSD model(0.373).The detection speed has been increased from 40 FPS to 50 FPS,fully proving the rationality of the optimized model.
作者
吴刚
罗炜
王小龙
朱晶晶
贾非
薛亚东
WU Gang;LUO Wei;WANG Xiaolong;ZHU Jingjing;JIA Fei;XUE Yadong(China Energy Engineering Group Jiangsu Power Design Institute Co.,Ltd.,Nanjing 211102;Department of Geotechnical Engineering,College of Civil Engineering,Tongji University,Shanghai 200092;Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education,Tongji University,Shanghai 200092)
出处
《现代隧道技术》
CSCD
北大核心
2023年第4期67-75,共9页
Modern Tunnelling Technology
基金
中国电力工程顾问集团有限公司科研项目(GSKJ2-G03-2021).
关键词
盾构隧道
表观病害
深度学习
目标检测
模型优化
Shield tunnel
Apparent defects
Deep learning
Target detection
Model optimization
