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Open Access

ARTICLE

Task-Specific YOLO Optimization for Railway Tunnel Cracks and Water Leakage: Benchmarking and Lightweight Enhancement

Yang Lei1,2, Kangshuo Zhu3,4,*, Bo Jiang1, Yaodong Wang3,4, Feiyu Jia1, Zhaoning Wang1, Falin Qi1, Qiming Qu1
1 Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing, China
2 National Superior College for Engineers, Beijing University of Aeronautics and Astronautics, Beijing, China
3 State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
4 School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China
* Corresponding Author: Kangshuo Zhu. Email: email
(This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077314

Received 06 December 2025; Accepted 15 January 2026; Published online 14 February 2026

Abstract

The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50 km/h. Subsequently, for the more complex water leakage detection task, this study proposes a novel lightweight enhancement to the most accurate model, YOLOv11s, by integrating the MobileNetV4 backbone and the Wise-IoU loss function. This optimization reduces computational load by 46.0% and increases speed by 17.4% to 33.97 FPS, theoretically supporting speeds up to 58.6 km/h. The main contributions of this work are twofold. First, this study conducts a systematic comparative analysis of YOLO series (v5 to v11) for distinct tunnel defect types (linear cracks vs. irregular water leakage), providing a clear selection guideline under strict speed constraints. Second, it introduces a novel, task-specific lightweight optimization paradigm, demonstrating that a one-model-fits-all approach is suboptimal for complex inspection tasks. Our study not only provides a practical solution but also establishes a valuable benchmark and optimization paradigm for real-time defect detection in tunnel engineering.

Keywords

Crack detection; railway tunnel; real-time detection; water leakage detection; YOLO

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