Nowadays, the detection technology of cracks in subway tunnels with increasingly developed rail transit in China is an important research direction. The subway will be a pillar of future traffic, so the safety of subway tunnels is also a top priority. Tunnel cracks are one of the most common problems. In the past, there were many methods for detecting cracks, but each has its own shortcomings, so this paper proposes a new algorithm. The purpose of the experiment in this paper is to identify tunnel cracks by designing an automatic tunnel crack detection system based on machine vision, in order to enhance the image using a combination of Mask homogenization and gray-scale corrosion, and use a Gaussian-fast median filter algorithm A large amount of noise is filtered out, and the crack image is segmented based on the Otsu method, and finally a binary image is obtained. The research results show that the proposed algorithm has a recognition rate of more than 95% for ordinary cracks, and an accuracy rate of 83% for the surface cracks of subway tunnels. Advantage. It is believed that this algorithm will greatly improve the safety of construction and train operation and protect people's lives.

Shimin Li. Crack Detection Technology of Subway Tunnel Based on Image Processing. International Journal of Engineering Technology and Construction (2023), Vol. 4, Issue 1: 18-30. https://doi.org/10.38007/IJETC.2023.040102.

[1] Bo Shen, Wen-Yu Zhang, Da-Peng Qi.Wireless Multimedia Sensor Network Based Subway Tunnel Crack Detection Method. International Journal of Distributed Sensor Networks, 2015, 2015(2):1-10. 

[2] Aside Noori Hoshyar, Sergey Kharkovsky, Bijan Samali. Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection. Journal of Signal & Information Processing, 2018, 9(2):111-121.

[3] Zhang, Zheng, Xu, Yong, Shao, Ling. Discriminative Block-Diagonal Representation Learning for Image Recognition. IEEE Transactions on Neural Networks & Learning Systems, 2017, 29(7):3111-3125.

[4] Huang, Xiaofei, Yang, Meng, Feng, Longlong. Crack detection study for hydraulic concrete using PPP-BOTDA. Smart Structures & Systems, 2017, 20(1):75-83.

[5] Youzhi Shi, Xiufang Li. Numerical Analysis on Influence of Subway Double-Hole Parallel Tunnel Deployment on Surrounding Soil Distortion. Open Civil Engineering Journal, 2015, 9(1):44-52.

[6] Fang Wang, Shu Zhang, Zuojun Tan. Non-destructive crack detection of preserved eggs using a machine vision and multivariate analysis. Wuhan University Journal of Natural Sciences, 2017, 22(3):257-262.

[7] Bin Chen, Yanan Wang, Zhaoli Yan. Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil. Sensors, 2018, 18(2):386.

[8] Rajeev R. Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System:. International Journal of Manufacturing Materials & Mechanical Engineering, 2016, 6(4):1-10.

[9] L-Q, Zhu, B Bai, Y-D Wang, etc. Subway tunnel crack identification algorithm based on feature analysis. Journal of the China Railway Society, 2015, 37(5):64-70.

[10] Jung-Bo Sim, Sang-Hee Woo, Se-Jin Yook. Baffle dust collector for removing particles from a subway tunnel during the passage of a train. Journal of Mechanical Science & Technology, 2018, 32(3):1415-1421.

[11] R Chen. Design and implementation of city subway tunnel section inspection. Journal of Geomatics, 2017, 42(1):115-118.

[12] Kyung-Young Jhang, Hogeon Seo. Nonlinear ultrasonic technique for closed crack detection. Journal of the Acoustical Society of America, 2015, 138(3):1836-1836.

[13] Joohyeb Song, Seulkirom Kim, Zhenyi Liu. A Real Time Nondestructive Crack Detection System for the Automotive Stamping Process. IEEE Transactions on Instrumentation & Measurement, 2016, 65(11):1-8.

[14] Wood H J, Albrecht R. Digital image processing of Ap-star coude Zeeman plates. Asia Europe Journal, 2015, 13(2):163-174.

[15] D Zhao, X Liu, Y Chen. Image recognition at night for apple picking robot. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(3):15-22.

[16] Herman G T, Marabini R, Carazo, José‐María, et al. Image processing approaches to biological three‐dimensional electron microscopy. International Journal of Imaging Systems & Technology, 2015, 11(1):12-29.

[17] Josef Pilc, Mário Drbúl, Dana Stančeková. Analysis of Potentiometric Methods Used for Crack Detection in Forging Tools. Technological Engineering, 2016, 12(1):27-30.

[18] V L Shkuratnik, P V Nikolenko, A A Kormnov. Estimation of ultrasonic correlation logging sensitivity in crack detection in excavation roof. Gornyi Zhurnal, 2016, 2016(1):54-57.

[19] Shao-Hu Peng, Hyun-Do Nam. A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images. Journal of Electrical Engineering & Technology, 2016, 11(4):1035-1041.

[20] Owada G, Nonaka T, Sato F, et al. Examination of the Detection Parameter for a Nondestructive Crack Detection System for Distribution Lines. Bmc Public Health, 2015, 15(1):1-7.

[21] Brooks, Will S M, Lamb, Dan A, Irvine, Stuart J C. IR Reflectance Imaging for Crystalline Si Solar Cell Crack Detection. IEEE Journal of Photovoltaics, 2017, 5(5):1271-1275.

[22] G P Bu, S Chanda, H Guan, Crack detection using a texture analysis-based technique for visual bridge inspection. Electronic Journal of Structural Engineering, 2015, 14(1):41-48.

[23] Pierre-yves Le Bas, Brian E Anderson, Marcel Remillieux. Elasticity Nonlinear Diagnostic method for crack detection and depth estimation. Journal of the Acoustical Society of America, 2015, 138(3):1836-1836.

[24] Maryam Zare. On crack detection in curved beams using change of natural frequency. Journal of Vibroengineering, 2018, 20(2):881-890.

[25] Tejas Kishor Patil, Prof, Ajeet B Bhane. Detection of Cracks in Simply Supported Beamby Using Various Techniques: A Review. International Journal of Research - GRANTHAALAYAH, 2015, 3(12):129-132.