Abstract
Continuous high-speed (>80 km/h) tunnel-image detection introduces new challenges to defect image acquisition, including machine recognition of entry and exit from a tunnel, storage of massive image data streams, image distortion due to surface photography, and encoder errors. In this study, an automatic image acquisition system is designed, and several critical technologies are proposed for high-speed rail tunnels. The system realizes quick tunnel identification and automatic camera control and uses the proposed line-scan software-matching method to obtain accurate images. Before storage, the real-time modification of the distorted images is implemented using an error-correction algorithm. An alternative mapping storage algorithm is proposed to improve the efficiency and stability of long-term storage. The test results show that the proposed method effectively reduces photographic errors. The lateral pixel-error rate of the corrected image is 1.60%, which is 10 times lower than that of the pre-correction, and the error rate of the longitudinal image is controlled within 10% when the system is moving at a variable speed and within 1% when it is moving at a constant speed. Furthermore, experiments have proven that the alternate mapping storage algorithm improves the storage efficiency of RAID by 50% and ensures data integrity; storage is accomplished by 8 HDDs at a camera throughput of 1.52 GB/s. This study will contribute to improvements in the speed and accuracy of tunnel defect-image detection.
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References
Ai Q, Yuan Y, Shen SL, Wang H, Huang X (2020) Investigation on inspection scheduling for the maintenance of tunnel with different degradation modes. Tunnelling and Underground Space Technology 106:103589, DOI: https://doi.org/10.1016/j.tust.2020.103589
Atif M, Khand ZH, Khan S, Akhtar F, Rajput A (2021) Storage optimization using adaptive thresholding motion detection. Engineering, Technology & Applied Science Research 11:6869–6872, DOI: https://doi.org/10.48084/etasr.3951
Attard L, Debono CJ, Valentino G, Di Castro M (2018) Vision-based change detection for inspection of tunnel liners. Automation in Construction 91: 142–154, DOI: https://doi.org/10.1016/j.autcon.2018.03.020
Attard L, Debono CJ, Valentino G, Di Castro M (2021) Vision-based tunnel lining health monitoring via bi-temporal image comparison and decision-level fusion of change maps. Sensors (Basel) 21:1–20, DOI: https://doi.org/10.3390/s21124040
Du L, Zhong R, Sun H, Zhu Q, Zhang Z (2018) Study of the integration of the CNU-TS-1 mobile tunnel monitoring system. Sensors (Switzerland) 18:1–16, DOI: https://doi.org/10.3390/s18020420
Filippidis C, Tsanakas P, Cotronis Y (2016) IKAROS: A scalable I/O framework for high-performance computing systems. Journal of Systems and Software 118:277–287, DOI: https://doi.org/10.1016/j.jss.2016.05.027
Gavilán M, Balcones D, Marcos O, Llorca DF, Sotelo MA, Parra I, Ocana M, Aliseda P, Yarza P, Amirola A (2011) Adaptive road crack detection system by pavement classification. Sensors 11:9628–9657, DOI: https://doi.org/10.3390/s111009628
Ghosh D, Kaabouch N (2016) A survey on image mosaicing techniques. Journal of Visual Communication and Image Representation 34:1–11, DOI: https://doi.org/10.1016/j.jvcir.2015.10.014
Gong Q, Zhu L, Wang Y, Yu Z (2021) Automatic subway tunnel crack detection system based on line scan camera. Structural Control and Health Monitoring 28:1–22, DOI: https://doi.org/10.1002/stc.2776
González JC, Martínez S, Jardón A, Balaguer C (2009) Robot-aided tunnel inspection and maintenance system. 2009 26th Int. Symp. Autom. Robot. Constr. ISARC 420–426, DOI: https://doi.org/10.22260/isarc2009/0066
Huang H, Li Q, Zhang D (2018) Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunnelling and Underground Space Technology 77:166–176, DOI: https://doi.org/10.1016/j.tust.2018.04.002
Huang H, Sun Y, Xue Y, Wang F (2017) Inspection equipment study for subway tunnel defects by grey-scale image processing. Advanced Engineering Informatics 32:188–201, DOI: https://doi.org/10.1016/j.aei.2017.03.003
Huang Z, Fu H, Fan X, Meng J, Chen W, Zheng X, Wang F, Zhang J (2021) Rapid surface damage detection equipment for subway tunnels based on machine vision system. Journal of Infrastructure Systems 27: 04020047, DOI: https://doi.org/10.1061/(asce)is.1943-555x.0000591
Jahanshahi MR, Masri SF (2012) Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Automation in Construction 22:567–576, DOI: https://doi.org/10.1016/j.autcon.2011.11.018
Jenkins MD, Carr TA, Iglesias MI, Buggy T, Morison G (2018) A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. European Signal Processing Conference 2018-Septe, 2120–2124, DOI: https://doi.org/10.23919/EUSIPCO.2018.8553280
Kim CN, Kawamura K, Shiozaki M, Tarighat A (2018) An image-matching method based on the curvature of cost curve for producing tunnel lining panorama. Journal of Japan Society of Civil Engineers. Springer Singapore, DOI: https://doi.org/10.2208/journalofjsce.6.1_78
Lee CH, Chiu YC, Wang TT, Huang TH (2013) Application and validation of simple image-mosaic technology for interpreting cracks on tunnel lining. Tunnelling and Underground Space Technology 34:61–72, DOI: https://doi.org/10.1016/j.tust.2012.11.002
Lee SY, Lee SH, Shin DI, Son YK, Han CS (2007) Development of an inspection system for cracks in a concrete tunnel lining. Canadian Journal of Civil Engineering 34:966–975, DOI: https://doi.org/10.1139/L07-008
Lei M, Liu L, Shi C, Tan Y, Lin Y, Wang W (2021) A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology 108:103724, DOI: https://doi.org/10.1016/j.tust.2020.103724
Leonidas E, Xu Y (2018) The development of an automatic inspection system used for the maintenance of rail tunnels. ICAC 2018–2018 24th IEEE Int. ICAC 2018–2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing 6–7, DOI: https://doi.org/10.23919/IConAC.2018.8749077
Li D, Xie Q, Gong X, Yu Z, Xu J, Sun Y, Wang J (2021) Automatic defect detection of metro tunnel surfaces using a vision-based inspection system. Advanced Engineering Informatics 47:101206, DOI: https://doi.org/10.1016/j.aei.2020.101206
Liao J, Yue Y, Zhang D, Tu W, Cao R, Zou Q, Li Q (2022) Automatic tunnel crack inspection using an efficient mobile imaging module and a lightweight CNN. IEEE Transactions on Intelligent Transportation Systems 1–14, DOI: https://doi.org/10.1109/TITS.2021.3138428
Liu X, Duan YJ, Xue CM, Liu B, Li Y (2018) Design and implement of vehicle-based experiment prototype for expressway tunnel intelligent detection. ACM International Conference Proceeding Series 78–81, DOI: https://doi.org/10.1145/3265639.3265686
Liu Y, Yao J, Lu X, Xie R, Li L (2019) DeepCrack: A deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338:139–153, DOI: https://doi.org/10.1016/j.neucom.2019.01.036
Lo SW, Kuo TW, Lam KY (2005) Multi-disk scheduling for time-constrained requests in RAID-0 devices. Journal of Systems and Software 76:237–250, DOI: https://doi.org/10.1016/j.jss.2004.05.029
Lu C, Cai C (2020) Overview on safety management and maintenance of high-speed railway in China. Transportation Geotechnics 25: 100397, DOI: https://doi.org/10.1016/j.trgeo.2020.100397
Menendez E, Victores JG, Montero R, Martínez S, Balaguer C (2018) Tunnel structural inspection and assessment using an autonomous robotic system. Automation in Construction 87:117–126, DOI: https://doi.org/10.1016/j.autcon.2017.12.001
Montero R, Victores JG, Martínez S, Jardón A, Balaguer C (2015) Past, present and future of robotic tunnel inspection. Automation in Construction 59: 99–112, DOI: https://doi.org/10.1016/j.autcon.2015.02.003
Neri A, Capua R, Salvatori P (2018) Track constrained RTK-like positioning for railway applications. Navigation, Journal of the Institute of Navigation 65:335–352, DOI: https://doi.org/10.1002/navi.260
Protopapadakis E, Stentoumis C, Doulamis N, Doulamis A, Loupos K, Makantasis K, Kopsiaftis G, Amditis A (2016) Autonomous robotic inspection in tunnels. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-5:167–174, DOI: https://doi.org/10.5194/isprsannals-iii-5-167-2016
Qi D, Liu Y, Gu Q, Zheng F (2015) Algorithm to detect the crack in the tunnel based on the image processing. Journal of Computers 26:11–19 (in Taiwan)
Song Q, Wu Y, Xin X, Yang L, Yang M, Chen H, Liu C, Hu M, Chai X, Li J (2019) Real-time tunnel crack analysis system via deep learning. IEEE Access 7:64186–64197, DOI: https://doi.org/10.1109/ACCESS.2019.2916330
Spinsante S, Stallo C (2020) Hybridized-GNSS approaches to train positioning: Challenges and open issues on uncertainty. Sensors (Switzerland) 20:1–17, DOI: https://doi.org/10.3390/s20071885
Stent S, Gherardi R, Stenger B, Soga K, Cipolla R (2016) Visual change detection on tunnel linings. Machine Vision and Applications 27:319–330, DOI: https://doi.org/10.1007/s00138-014-0648-8
Stent SAI, Girerd C, Long PJG, Cipolla R (2015) A low-cost robotic system for the efficient visual inspection of tunnels. 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings, DOI: https://doi.org/10.22260/isarc2015/0070
Sun H, Xu Z, Yao L, Zhong R, Du L, Wu H (2020) Tunnel monitoring and measuring system using mobile laser scanning: Design and deployment. Remote Sensing, 12, DOI: https://doi.org/10.3390/rs12040730
Victores JG, Martínez S, Jardón A, Balaguer C (2011) Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration. Automation in Construction 20:629–636, DOI: https://doi.org/10.1016/j.autcon.2010.12.005
Wang M, Zhang Q (2020) Optimized data storage algorithm of IoT based on cloud computing in distributed system. Computer Communications 157:124–131, DOI: https://doi.org/10.1016/j.comcom.2020.04.023
Yang E, Liu Y, Liu S (2020) An efficient lightweight object detector for railway tunnel safety monitoring. International Conference on Signal Processing. Proceedings, ICSP 2020-Decem, 376–380, DOI: https://doi.org/10.1109/ICSP48669.2020.9320914
Yang J, Feng QB (2013) A new method for measuring subgrade settlement in high-speed railway by using a linear CCD. Measurement 46:1751–1756, DOI: https://doi.org/10.1016/j.measurement.2012.11.049
Yu SN, Jang JH, Han CS (2007) Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Automation in Construction 16:255–261, DOI: https://doi.org/10.1016/j.autcon.2006.05.003
Zhang W, Zhang Z, Qi D, Liu Y (2014) Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors (Switzerland) 14:19307–19328, DOI: https://doi.org/10.3390/s141019307
Zhao S, Zhang D, Xue Y, Zhou M, Huang H (2021) A deep learning-based approach for refined crack evaluation from shield tunnel lining images. Automation in Construction 132:103934, DOI: https://doi.org/10.1016/j.autcon.2021.103934
Zhao Y, He H, Li P (2018) Key techniques for the construction of high-speed railway large-section loess tunnels. Engineering 4:254–259, DOI: https://doi.org/10.1016/j.eng.2017.07.003
Zheng Y, He S, Yu Y, Zheng J, Zhu Y, Liu T (2021) Characteristics, challenges and countermeasures of giant karst cave: A case study of Yujingshan tunnel in high-speed railway. Tunnelling and Underground Space Technology, 114, DOI: https://doi.org/10.1016/j.tust.2021.103988
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This study is supported by the National Natural Science Foundation of China (Grant No. 51978582).
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Qin, S., Qi, T., Lei, B. et al. Rapid and Automatic Image Acquisition System for Structural Surface Defects of High-Speed Rail Tunnels. KSCE J Civ Eng 28, 967–989 (2024). https://doi.org/10.1007/s12205-023-1775-4
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DOI: https://doi.org/10.1007/s12205-023-1775-4