[1]胡珉,周显威,高新闻.公路隧道视频预处理和病害识别算法[J].华侨大学学报(自然科学版),2020,41(5):595-604.[doi:10.11830/ISSN.1000-5013.202002024]
　HU Min,ZHOU Xianwei,GAO Xinwen.Video Preprocess and Defect Recognition Algorithm for Road Tunnel[J].Journal of Huaqiao University(Natural Science),2020,41(5):595-604.[doi:10.11830/ISSN.1000-5013.202002024]
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公路隧道视频预处理和病害识别算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第41卷
期数:
2020年第5期
页码:
595-604
栏目:
出版日期:
2020-09-20

文章信息/Info

Title:
Video Preprocess and Defect Recognition Algorithm for Road Tunnel
文章编号:
1000-5013(2020)05-0595-10
作者:
胡珉1; 2;  周显威1; 2;  高新闻2; 3
1. 上海大学 悉尼工商学院, 上海 201800;2. 上海大学 上海城建集团建筑产业化研究中心, 上海 200072;3. 上海大学 机电工程与自动化学院, 上海 200444
Author(s):
HU Min1; 2;  ZHOU Xianwei1; 2;  GAO Xinwen2; 3
1. SHU-UTS SILC Business School, Shanghai University, Shanghai 201800, China; 2. SHU-SUCG Research Center, Shanghai University, Shanghai 200072, China; 3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
关键词:
公路隧道;  隧道病害;  图像预处理;  目标识别;  深度学习
Keywords:
road tunnel;  tunnel defects;  image preprocess;  object identification;  deep learning
分类号:
TP274
DOI:
10.11830/ISSN.1000-5013.202002024
文献标志码:
A
摘要:
基于计算机视觉技术,针对公路隧道病害进行检测与识别,提出视频数据的预处理方法.使用全卷积网络(FCN)模型识别病害的类别和位置,融合不同的上采样结果使最终结果更加精细,结合马尔可夫随机场(MRF)增强FCN模型的空间一致性.实验结果表明:该方法可解决数据冗余、镜头畸变及样本不均衡等问题;该方法在上海市虹梅南路隧道中的应用结果验证其准确度与可靠性.
Abstract:
Based on computer vision, the defect detection and identification were conducted for road tunnel. A preprocess method for video data was proposed. Finally, fully convolutional networks(FCN)model was used to identify the category and location of defects. Different up-sampling results were integrated to make the final results more precisely, and the spatial consistency of FCN model was improved by combining Markov random field(MRF). The experimental results show that this method can solve the problems of data redundancy, lens distortion and sample imbalance. The application of this method in the Shanghai Hongmei South Road Tunnel in Shanghai validates its accuracy and reliability.

参考文献/References:

[1] AI Qing,YUAN Yong,BI Xiangli.Acquiring sectional profile of metro tunnels using charge-coupled device cameras[J].Structure and Infrastructure Engineering,2016,12(9):1065-1075.DOI:10.1080/15732479.2015.1076855.
[2] 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(32):188-201.DOI:10.1016/j.aei.2017.03.003.
[3] SASAMA H.Maintenance of railway facilities by continuously scanned image inspection[J].Japanese Railway Engineering,1994,33(2):1-5.
[4] HAAS C,SKIBNIEWSKI M,BUDNY E.Robotics in civil engineering[J].Computer-Aided Civil and Infrastructure Engineering,1995,10:371-381.DOI:10.1111/j.1467-8667.1995.tb00298.x.
[5] UKAI M,MIYAMOTO T,SASAMA H.Development of inspection system of railway facilities using continuous scan image[J].Computers in Railways,1996,20(18):61-70.DOI:10.2495/CR960071.
[6] SASAMA H,UKAI M,OHTA M,et al.Inspection system for railway facilities using a continuously scanned image[J].Electrical Engineering in Japan,1998,125(2):52-64.DOI:10.1002/(SICI)1520-6416(19981115)125:23.0.CO; 2-N.
[7] UIAI M.Technology for measurement and diagnosis development of image processing technique for detection of tunnel wall deformation using continuously scanned image[J].Quarterly Report of RTRI,2000,41(3):120-126.DOI:10.2219/rtriqr.41.120.
[8] LEE S Y,LEE S H,SHIN D I,et al.Development of an inspection system for cracks in a concrete tunnel lining[J].Canadian Journal of Civil Engineering,2007,34(8):966-975.DOI:10.1139/l07-008.
[9] UKAI M.Advanced inspection system of tunnel wall deformation using image processing[J].Quarterly Report of RTRI,2007,48(2):94-98.DOI:10.2219/rtriqr.48.94.
[10] MONTERO R,VICTORES J G,MARTINEZ S,et al.Past, present and future of robotic tunnel inspection[J].Automation in Construction,2015,59:99-112.DOI:10.1016/j.autcon.2015.02.003.
[11] LATTANZI D,MILLER G.Review of robotic infrastructure inspection systems[J].Journal of Infrastructure Systems,2017,23(3):04017004.1-04017004.16.DOI:10.1061/(asce)is.1943-555x.0000353.
[12] HAACK A,SCHREYER J,JACKEL G.State-of-the-art of non-destructive testing methods for determining the state of a tunnel lining[J].Tunnelling and Underground Space Technology,1995,10(4):413-431.DOI:10.1016/0886-7798(95)00030-3.
[13] RICHARDS J A.Inspection, maintenance and repair of tunnels: International lessons and practice[J].Tunnelling and Underground Space Technology,1998,13(4):369-375.DOI:10.1016/S0886-7798(98)00079-0.
[14] ASAKURA T,KOJIMA Y.Tunnel maintenance in Japan[J].Tunnelling and Underground Space Technology,2003,18(2):161-169.DOI:10.1016/S0886-7798(03)00024-5.
[15] DELATTE N J,CHEN S E,MAINI N,et al.Application of nondestructive evaluation to subway tunnel systems[J].Transportation Research Record Journal of the Transportation Research Board,2003,1845(3):127-135.DOI:10.3141/1845-14.
[16] LEE C H,CHIU Y C,WANG T T,et al.Application and validation of simple image-mosaic technology for interpreting cracks on tunnel lining[J].Tunnelling and Underground Space Technology,2013,34:61-72.DOI:10.1016/j.tust.2012.11.002.
[17] DAWOOD T,ZHU Z,ZAYED T.Machine vision-based model for spalling detection and quantification in subway networks[J].Automation in Construction,2017,81:149-160.DOI:10.1016/j.autcon.2017.06.008.
[18] WU Xuezhen,JIANG Yujing,WANG Jianhua,et al.A new health assessment index of tunnel lining based on the digital inspection of surface cracks[J].Applied Sciences,2017,7(5):507.DOI:10.3390/app7050507.
[19] KONISHI S,KAWAKAMI K,TAGUCHI M.Inspection method with infrared thermometry for detect void in subway tunnel lining[J].Procedia Engineering,2016(165):474-483.DOI:10.1016/j.proeng.2016.11.723.
[20] ZHANG Lei, YANG Fan, ZHANG Yimin, et al.Road crack detection using deep convolutional neural network[C]//IEEE International Conference on Image Processing(ICIP 2016).[S.l.]:IEEE Press,2016:3708-3712.DOI:10.1109/ICIP.2016.7533052.
[21] CHA Y J,CHOI W,BUYUKOZTURK O.Deep learning-based crack damage detection using convolutional neural networks[J].Computer Aided Civil & Infrastructure Engineering,2017,32(5):361-378.DOI:10.1111/mice.12263.
[22] XU Yang,LI Shunlong,ZHANG Dongyu,et al.Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images[J].Structural Control and Health Monitoring,2018,25(2):e2075.DOI:10.1002/stc.2075.
[23] YANG Tao,WUYan,ZHAO Junqiao,et al.Semantic segmentation via highly fused convolutional network with multiple soft cost functions[J].Cognitive Systems Research,2018,53:20-30.DOI: 10.1016/j.cogsys.2018.04.004
[24] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,39(4):640-651.DOI:10.1109/CVPR.2015.7298965.
[25] ZHENG Shuai,JAYASUMANA S,ROMERA-PAREDES B,et al.Conditional random fields as recurrent neural networks[C]//IEEE International Conference on Computer Vision.Santiago:IEEE Press,2015:1529-1537.DOI:10.1109/ICCV.2015.179.
[26] LIU Ziwei,LI Xiaoxiao,LUO Ping,et al.Semantic image segmentation via deep parsing network[C]//IEEE International Conference on Computer Vision.Santiago:IEEE Press,2015:1377-1385.DOI:10.1109/ICCV.2015.162.
[27] 郝晓丽,高永.CUDA框架下的视频关键帧互信息熵多级提取算法[J].电子科技大学学报,2018,47(5):726-732.DOI:10.3969/j.issn.1001-0548.2018.05.014.
[28] 王疏华,巨志勇,彭彦妮.基于图像熵的全局和局部混合方法的关键帧提取[J].软件导刊,2018,17(2):213-215,218.DOI:10.11907/rjdk.172485.
[29] ABDEL-QADER I M,BAZUIN B J,MOUSAVINEZHAD H S,et al.Real-time digital signal processing in the undergraduate curriculum[J].IEEE Transactions on Education,2003,46(1):95-101.DOI:10.1109/te.2002.808273.
[30] 薛亚东,李宜城.基于深度学习的盾构隧道衬砌病害识别方法[J].湖南大学学报(自然科学版),2018,45(3):100-109.DOI:10.16339/j.cnki.hdxbzkb.2018.03.012.

备注/Memo

备注/Memo:
收稿日期: 2020-02-24
通信作者: 胡珉(1970-),女,副教授,博士,主要从事智能信息处理和建筑信息化的研究.E-mail:minahu@163.com.
基金项目: 上海市科委重点项目(18DZ1201204)
更新日期/Last Update: 2020-09-20