A Deep Learning-Driven Framework for Real-Time Recognition and Quantification of Subway Tunnel Surface Defects Using High-Resolution Imaging

Abstract

Subway tunnels, with their complex underground environment, frequently develop surface defects like cracks and water leakage over time. Traditional manual inspection methods are inadequate for current accuracy and efficiency needs. This paper introduces a deep learning approach to detect and assess these defects in subway tunnels, aiming for real-time detection and quantitative assessment. It proposes a data collection strategy for rapid detection and detailed exploration in subway shield tunnels. A multi-category dataset of tunnel defects was created, alongside an automatic identification method using the YOLO v7 algorithm for operational tunnels. This method, validated against Qingdao Metro’s manual inspection records, markedly reduces manual inspection costs during tunnel operation. Research indicates a strong correlation between image inspection equipment efficiency, defect detection accuracy, and actual project needs in subway tunnels. Image quality is positively linked to tunnel illumination intensity. A balance between inspection speed and focal length is crucial for image size and precision. Lowering the confidence threshold from 0.4 to 0.2 increases detection rates for cracks and water leakage by 20.60 and 4.06%, respectively, minimizing defect oversight. This study presents algorithms, frameworks, and methods for real-time quantification to enhance tunnel operation, maintenance, and manual processing.

Keywords

Object detection / Image recognition / Tunnel surface defects / Machine learning

References

[1] FlahM, NunezI, ChaabeneWB, NehdiML. Machine learning algorithms in civil structural health monitoring: a systematic review. Arch Comput Methods Eng, 2021, 28(4): 2621-2643.

[2] PasadasDJ, BaskaranP, RamosHG, RibeiroAL. Detection and classification of defects using ECT and multi-level SVM model. IEEE Sens J, 2020, 20(5): 2329-2338.

[3] ProtopapadakisE, VoulodimosA, DoulamisA. On the impact of labeled sample selection in semisupervised learning for complex visual recognition tasks. Complexity, 2018.

[4] SavinoP, TondoloF. Automated classification of civil structure defects based on convolutional neural network. Front Struct Civ Eng, 2021, 15(2): 305-317.

[5] ChenX, ZhangQ, LiuR, WangX, HeW. Maintenance strategies and life-cycle cost analysis of inspection robots in metro tunnels. Tunn Undergr Space Technol, 2023, 140105270.

[6] MaksymovychP, YanJ, SalesB, WangJ. Atomic-scale analysis of disorder by similarity learning from tunneling spectroscopy. Phys Rev Res, 2022, 43033058.

[7] TabriziK, CelayaM, MillerBS, WittwerA, RuzziL. Damage assessment of tunnel lining by mobile laser scanning: pittsburgh, pennsylvania, implementation phase of FHWA SHRP 2 R06G project. Transp Res Rec J Transp Res Board, 2017, 2642(1): 166-179.

[8] AbbasN, UmarT, SalihR, AkbarM, HussainZ, HaibeiX. Structural health monitoring of underground metro tunnel by identifying damage using ANN deep learning auto-encoder. Appl Sci, 2023, 1331332.

[9] ZhangW, ZhangZ, QiD, LiuY. Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors, 2014, 14(10): 19307-19328.

[10] MonteroR, VictoresJG, MartínezS, JardónA, BalaguerC. Past, present and future of robotic tunnel inspection. Autom Constr, 2015, 59: 99-112.

[11] HuangH, SunY, XueY, WangF. Inspection equipment study for subway tunnel defects by grey-scale image processing. Adv Eng Inform, 2017, 32: 188-201.

[12] HuangH-W, LiQ-T, ZhangD-M. Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunn Undergr Space Technol, 2018, 77: 166-176.

[13] RenY, HuangJ, HongZ, WeiLu, YinJ, ZouL, ShenX. Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr Build Mater, 2020, 234117367.

[14] ZhouZ, ZhangJ, GongC. Automatic detection method of tunnel lining multi-defects via an enhanced you only look once network. Comput-Aided Civ Infrastruct Eng, 2022, 37(6): 762-780.

[15] KowalikMP, PelowskiF, LipowieckiI. Test and comparison of different digitalization techniques for objects with surfaces difficult to scan. Adv Sci Technol Res J., 2025, 19(2): 1-15.

[16] ChenZ, ZhaoHM, HaoXS, YuanB, LiX. STViT plus : improving self-supervised multi-camera depth estimation with spatial-temporal context and adversarial geometry regularization. Appl Intell, 2025, 555328.

[17] AbbasN, UmarT, SalihR, AkbarM, HussainZ, HaibeiX. Structural health monitoring of underground metro tunnel by identifying damage using ANN deep learning auto-encoder. Appl Sci-Basel, 2023, 1331332.

[18] AslamB, UmarT. Automated code compliance checking through building information modelling. Proc Inst Civ Eng Struct Build, 2023, 177(9): 822-837.

[19] RehmanA, UmarT. Literature review: industry 5.0. leveraging technologies for environmental, social and governance advancement in corporate settings. Corp Gov Int J Bus Soc, 2025, 25(2): 229-251.

[20] TalabAM, AhmedZH, XiF, HaiMingL. Detection crack in image using otsu method and multiple filtering in image processing techniques. Optik, 2016, 127(3): 1030-1033.

[21] LiD, XieQ, GongX, ZhenghaoYu, JinxuanXu, SunY, WangJ. Automatic defect detection of metro tunnel surfaces using a vision-based inspection system. Adv Eng Inform, 2021, 47101206.

[22] WangZ, SuiQ, GuoW, ZhangJ, LiZ, YangY, ShangY, ZangL, ShaohuaiYu. Deep learning based distortion restoration and defect segmentation from linear scanning camera images. Meas Sci Technol, 2023, 345055402.

[23] XueY, LiY. A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects. Comput Aided Civ Infrastruct Eng, 2018, 33(8): 638-654.

[24] BarcetF, TualM, FoucherP, CharbonnierPUsing machine learning on depth maps and images for tunnel equipment surveying2022Int Arch Photogramm Remote Sens Spat Inform Sci

[25] GengP, JiaM, RenX. Tunnel lining water leakage image segmentation based on improved BlendMask. Struct Health Monit, 2023, 22(2): 865-878.

[26] GuoD, Jinhui LiXu, LiZL, LiP, ChenZ. Advance prediction of collapse for tbm tunneling using deep learning method. Eng Geol, 2022, 299106556.

[27] XueY, BaiC, QiuD, KongF, LiZ. Predicting rockburst with database using particle swarm optimization and extreme learning machine. Tunn Undergr Space Technol, 2020, 98103287.

[28] Lakshmi NarayanaC, RamanaKV. An efficient real-time weed detection technique using YOLOv7. Int J Adv Comput Sci Appl, 2023.

[29] JoshiD, SinghTP, SharmaG. Automatic surface crack detection using segmentation-based deep-learning approach. Eng Fract Mech, 2022, 268108467.

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