基于机器学习方法的路面病害检测研究综述

邹政; 陈江; 郎洪; 王笑风; 万晨光; 丁朔; 陆键

doi:10.3963/j.jssn.1674-4861.2025.02.016

基于机器学习方法的路面病害检测研究综述

doi: 10.3963/j.jssn.1674-4861.2025.02.016

邹政^1,,
陈江¹,
郎洪^1, ,,
王笑风²,
万晨光²,
丁朔¹,
陆键^{1, 3}

1.
同济大学道路与交通工程教育部重点实验室上海 201804
2.
河南省中工设计研究院集团股份有限公司郑州 450000
3.
山东交通学院济南 250357

基金项目:

国家自然科学基金项目 62206201

河南省交通运输厅科技项目 2023-1-1

中国博士后科学基金项目 2023M732644

详细信息

作者简介:
邹政(1993—)，博士研究生. 研究方向：智能交通系统工程. E-mail: zouzhn@tongji.edu.cn

通讯作者:
郎洪(1994—)，博士，副研究员. 研究方向：交通信息工程及控制、全域感知等. E-mail: honglang@to-gji.edu.cn

中图分类号: U416.2
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- 文章访问数: 44
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- 被引次数: 0
出版历程
- 收稿日期: 2024-05-24
- 网络出版日期: 2025-09-29

A Review of Pavement Distress Detection Based on Machine Learning Methods

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

摘要

摘要: 道路路面的质量状况直接影响其使用寿命和行车安全。路面病害具有机理复杂、形态多样的特点，且在空间尺度上呈现出大样本分布，给度量与标注带来较大挑战，传统检测方法难以满足多场景、多类型病害识别的实际应用需求。本文回顾了机器学习方法在路面病害检测中的重要研究成果，比较了不同识别方法的技术原理和适用性，并总结了公开和私有的路面病害图像数据集。然后，基于三维深度数据对路面病害展开多种特征分析，为挖掘路面病害多元特征与人工智能模型的相互作用提供算法基础。最后，系统性介绍了通用的客观评测指标以保证路面病害智能算法评价的公正性。病害识别经历了传统的阈值分割、面元分类和目标检测并逐步发展到像素级图像分割的趋势，自动识别算法的准确性和通用性得到了大幅度提升。传统机器学习方法和深度学习方法之间仍然可以相互借鉴与融合，以覆盖更多的路面病害类型并促使模型能够学习到更多有效特征，从而提高病害检测精度及效率。现有不同识别算法应以运行时间、显存占比、运算量、识别率作为一致的性能评价指标，并考虑不同道路环境下不同维度的路面病害数据集。未来应研发适应复杂工况的检测与处理方法，提升信噪比与模型泛化能力，推动智能算法在多场景中的落地应用。
- 道路工程 /
- 病害检测 /
- 机器学习 /
- 像素分割 /
- 评价方法
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.
- road engineering /
- distress detection /
- machine learning /
- pixel segmentation /
- evaluation method

HTML全文

图 1 深度学习对路面病害图像的处理方式

Figure 1. The processing approach of pavement distress images using deep learning

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图 2 8个裂缝类路面病害数据集

Figure 2. Eight datasets of crack-type pavement distresses

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图 3 3个多类型路面病害数据集

Figure 3. Three datasets of multiple types of pavement distresses

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图 4 8组不同的路面病害三维图像

Figure 4. Eight sets of different 3D images of pavement distresses

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图 5 裂缝类路面病害的横断面高程曲线

Figure 5. Transverse elevation profiles of crack-type pavement distresses

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图 6 变形类路面病害的横断面高程曲线

Figure 6. Transverse elevation profiles of deformation-type pavement distresses

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图 7 路面病害三维图像的灰度直方图（图 4数据）

Figure 7. Grayscale histogram of 3D pavement distress images (data from Fig. 4)

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图 8 路面病害三维图像的灰度等高线（图 4数据）

Figure 8. Grayscale contour lines of 3D pavement distress images (data from Fig. 4)

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图 9 路面病害三维图像的傅立叶频域分析结果（图 4数据）

Figure 9. Fourier frequency domain analysis results of 3D pavement distress images(data from Fig. 4)

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图 10 Canny边缘检测算子检测结果（图 4数据）

Figure 10. Detection results using the Canny edge detection operator (data from Fig. 4)

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表 1 公开的主流路面病害数据集的基本信息

Table 1. Basic information of publicly available mainstream pavement distress datasets

类型	数据集	数量	尺寸	光源	相机	标注方式
裂缝类病害	CrackTree260	260	800×600	可见光	面阵	像素级
	Crack500	500	2 000×1 500	可见光	手机	像素级
	CrackLS315	315	512×512	激光	线阵	像素级
	CRKWH100	100	512×512	可见光	线阵	像素级
	CFD	118	480×320	可见光	手机	像素级
	AigleRN	38	991×462 311×462	可见光	面阵	像素级
	GAPs384	384	1 920×1 080	激光	线阵	像素级
	AIMCrack	527	1 920×1 080	可见光	行车记录仪	像素级
多种病害类型	GAPs	1 969	1 920×1 080	激光	线阵	边界框
	GAPs v2	2 468	1 920×1 080	激光	线阵	边界框
	Road Damage	9 053	600×600	可见光	手机	边界框
	ISTD-PDS7	18527		HID电灯	面阵	像素级/二值

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表 2 三维图像全局纹理特征

Table 2. Global texture features of 3D images

图像序号	类型	相对深度	平均对比度	平滑度	偏态性	一致性	熵值	$\left\\|\boldsymbol{T}_i-\overline{\boldsymbol{T}}\right\\|$
A1	完好路面	0.212 6	0.107	0.381	0.680 4	0.601 4	0.248 2	0.182 7
A2	完好路面	0.493 9	0.364 9	0.761 9	0.619 5	0.210 1	0.682	0.328 5
B1	松散类	1	0.306 1	0.714 3	0	0.956 5	0.067 5	1
B2	松散类	0.658 3	0.323	0.714 3	0.713	0.239 1	0.627 7	0.052 9
C1	线裂	0.352	0	0	0.746 7	0.623 2	0.218 7	0.113 4
C2	线裂	0.091 6	0.654 2	0.904 8	0.850 8	0.058	0.849 4	0.371 8
D1	网状裂缝	0.1	0.460 7	0.809 5	1	0.268 1	0.653 2	0.586 5
D2	网状裂缝	0	0.334 7	0.714 3	0.793 3	0.340 6	0.497 9	0.254
E1	变形类	0.405 4	0.720 3	0.952 4	0.491 5	0	1	0.530 7
E2	变形类	0.391 1	1	1	0.196 3	0.152 2	0.850 9	0.841 5
F1	沥青修补	0.555 3	0.162 5	0.476 2	0.678 5	0.485 5	0.353 8	0.189
F2	沥青修补	0.432 8	0.141 2	0.428 6	0.857 9	0.644 9	0.317 9	0.384 4
G1	白色标线	0.527 2	0.541 2	0.857 1	0.569 1	0.188 4	0.722 8	0.418 1
G2	白色标线	0.333 1	0.362 6	0.761 9	0.727	0.195 7	0.708 1	0
H1	桥接缝	0.515 2	0.236 4	0.619	0.552 7	0.405 8	0.479 2	0.443 8
H2	桥接缝	0.913 6	0.208 3	0.571 4	0.299 2	1	0	0.746 6

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