基于地铁隧道高分辨率图像的裂缝信息提取

doi:10.13474/j.cnki.11-2246.2024.0415

摘要/Abstract

摘要： 随着运营年限的增长,地铁隧道会逐渐产生各类病害,裂缝是一种典型的病害现象。由于大部分裂缝的特征不突出,且受到地铁隧道内部电缆设备、划痕、蜘蛛网等线状干扰物的影响,现有裂缝检测方法在高分辨率图像下效果不佳。本文以衬砌裂缝为研究对象,基于自主研发的隧道相机系统,实现对隧道表面信息的无损数据采集,获取4096×2168像素的高分辨率图像数据;明确裂缝识别的干扰因素,基于病害特性构建干扰数据集及真实纹理数据集;以掩码-区域卷积神经网络(Mask R-CNN)模型为基准框架,采用K-means和遗传算法对区域建议网络(RPN)网络进行参数优化;利用对比试验和消融试验说明本文算法的检测效果和表现性能。结果表明,本文算法能实现高分辨率图像下隧道裂缝的识别和长度测量,漏检和误检概率更低,对细长、特征不太明显的裂缝具有较好的检测性能,裂缝测量值可为地铁的运营维护提供参考。

关键词: Mask R-CNN, 高分辨率图像, 尺寸聚类, 裂缝, 长度测量

Abstract: Due to the fact that most of the cracks do not have distinctive features and are affected by cable,scratches,cobwebs and other linear interferences inside the tunnels,the detection effect of the existing crack detection methods,using high-resolution images,still needs to be improved. This paper takes the lining cracks as the research object,realizes the non-destructive data acquisition of the tunnel surface information based on the tunnel camera system,and acquires the high-resolution image data of 4096×2168 pixels. And we clarify the interference factors of crack identification,and constructs the interference data set and the real texture data set based on the characteristics of the disease; takes the Mask R-CNN model as the baseline framework,and adopts the K-means and genetic algorithm to optimize the parameters of RPN network. The detection effect and performance of this paper's algorithm are illustrated using comparative and ablation experiments. The results show that the algorithm proposed in this paper can realize the recognition and length measurement of tunnel cracks under high-resolution images,with lower probability of leakage and false detection,and has better detection performance for the slender and less obvious cracks,and the measured values of the cracks can provide reference information for the operation and maintenance of the subway.

Key words: Mask R-CNN, high-resolution images, size clustering, crack, length measurement

中图分类号:

P258

魏向辉, 孙亮, 赵烁阳. 基于地铁隧道高分辨率图像的裂缝信息提取[J]. 测绘通报, 2024, 0(4): 90-95.

WEI Xianghui, SUN Liang, ZHAO Shuoyang. Crack information extraction based on high-resolution images of metro tunnels[J]. Bulletin of Surveying and Mapping, 2024, 0(4): 90-95.

参考文献

[1] SHEN Bo,ZHANG Wenyu,QI Dapeng,et al. Wireless multimedia sensor network based subway tunnel crack detection method[J]. International Journal of Distributed Sensor Networks,2015,11(6):1-10.
[2] 李健超,张翠兵,柴雪松,等. 基于图像识别技术的隧道衬砌裂缝检测系统研究[J]. 铁道建筑,2018,58(1):20-24.
[3] HUANG Hongwei,SUN Yan,XUE Yadong,et al. Inspection equipment study for subway tunnel defects by grey-scale image processing[J]. Advanced Engineering Informatics,2017,32(C):188-201.
[4] WANG Quanlei,ZHANG Ning,JIANG Kun,et al. Tunnel lining crack recognition based on improved multiscale retinex and sobel edge detection[J]. Mathematical Problems in Engineering,2021(41): 9969464.
[5] 张振海,尹晓珍,王阳萍,等. 基于特征分析的图像式地铁隧道裂缝检测方法研究[J]. 铁道科学与工程学报,2019,16(11):2791-2800.
[6] REZAIE A,ACHANTA R,GODIO M,et al. Comparison of crack segmentation using digital image correlation measurements and deep learning[J]. Construction and Building Materials,2020,261:120474.
[7] AN Qing,CHEN Xijiang,WANG Haojun,et al. Segmentation of concrete cracks by using fractal dimension and UHK-net[J]. Fractal and Fractional,2022,6(2):95.
[8] 方广欣. 基于YOLOv5的地铁隧道裂缝检测技术研究[D]. 合肥:合肥工业大学,2021.
[9] 薛亚东,高健,李宜城,等. 基于深度学习的地铁隧道衬砌病害检测模型优化[J]. 湖南大学学报(自然科学版),2020,47(7):137-146.
[10] 折昌美. 地铁隧道复杂裂缝病害的图像识别算法研究[D].北京:北京交通大学,2019.
[11] DUNG C V,ANH L D. Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction,2019,99:52-58.
[12] LIU Zhenqing,CAO Yiwen,WANG Yize,et al. Computer vision-based concrete crack detection using U-net fully convolutional networks[J]. Automation in Construction,2019,104:129-139.
[13] 王耀东,余祖俊,白彪,等. 基于图像处理的地铁隧道裂缝识别算法研究[J]. 仪器仪表学报,2014,35(7):1489-1496.
[14] 陈占龙,李双江,徐永洋,等. 高分影像密集建筑物Correg-YOLOv3检测方法[J]. 测绘学报,2022,51(12):2531-2540.
[15] REN Yupeng,HUANG Jisheng,HONG Zhiyou,et al. Image-based concrete crack detection in tunnels using deep fully convolutional networks[J]. Construction and Building Materials,2020,234:117367.
[16] XU Yingying,LI Dawei,XIE Qian,et al. Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN[J]. Measurement,2021,178:109316.
[17] XUE Yadong,LI Yicheng. A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects[J]. Computer-Aided Civil and Infrastructure Engineering,2018,33(8):638-654.
[18] 李梓豪,唐超. 基于高清工业相机的盾构隧道裂缝智能识别算法分析[J]. 测绘通报,2021(8):83-87.
[19] LI Shengyuan,ZHAO Xuefeng,ZHOU Guangyi. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering,2019,34(7):616-634.