Volume 43 Issue 2
Apr.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(2): 154-168
ZOU Zheng, CHEN Jiang, LANG Hong, WANG Xiaofeng, WAN Chenguang, DING Shuo, LU Jian. A Review of Pavement Distress Detection Based on Machine Learning Methods[J]. Journal of Transport Information and Safety, 2025, 43(2): 154-168. doi: 10.3963/j.jssn.1674-4861.2025.02.016
Citation: ZOU Zheng, CHEN Jiang, LANG Hong, WANG Xiaofeng, WAN Chenguang, DING Shuo, LU Jian. A Review of Pavement Distress Detection Based on Machine Learning Methods[J]. Journal of Transport Information and Safety, 2025, 43(2): 154-168. doi: 10.3963/j.jssn.1674-4861.2025.02.016
shu

A Review of Pavement Distress Detection Based on Machine Learning Methods

doi: 10.3963/j.jssn.1674-4861.2025.02.016
  • 1.

    The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

  • 2.

    Henan Zhonggong Design and Research Institute Group Co., Ltd., Zhengzhou 450000, China

  • 3.

    Shandong Jiaotong University, Jinan 250357, China

  • Received Date: 2024-05-24
    Available Online: 2025-09-29
    Abstract
  • Road surface quality directly influences service life and driving safety. Pavement distress involves complex mechanisms, diverse forms, and large-scale spatial distribution, posing challenges for measurement and annotation. Traditional detection methods struggle to meet practical needs for identifying various distress types in diverse scenarios. This paper reviews achievements in machine learning-based pavement distress detection, compares principles and applicability of recognition methods, and summarizes public and private image datasets of pavement distress. Subsequent feature analyses of pavement distress using 3D depth data provide a basis for exploring interactions between multidimensional features and AI models. Finally, general objective evaluation metrics are systematically introduced to ensure fairness in AI-based pavement distress assessment. Distress recognition has evolved from threshold segmentation, element classification, and object detection to pixel-level segmentation, significantly improving accuracy and generalizability. Traditional machine learning and deep learning methods can be integrated to detect more distress types and learn more effective features, improving accuracy and efficiency. Current algorithms should be evaluated using consistent metrics, including runtime, memory usage, computational load, and recognition rate, while considering datasets with varying distress dimensions and road environments. Future work should develop detection and processing methods for complex conditions, enhance signal-to-noise ratios and generalization, and support deploying intelligent algorithms in diverse scenarios.

     

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