路面破损图像自动处理技术研究进展

徐志刚; 车艳丽; 李金龙; 赵祥模; 潘勇; 王忠仁; 韦娜; 宋宏勋

doi:10.19818/j.cnki.1671-1637.2019.01.017

路面破损图像自动处理技术研究进展

doi: 10.19818/j.cnki.1671-1637.2019.01.017

徐志刚^1,,
车艳丽²,
李金龙¹,
赵祥模¹,
潘勇^{1, 3},
王忠仁⁴,
韦娜¹,
宋宏勋¹

1.
长安大学信息工程学院，陕西西安 710064
2.
石家庄华燕交通科技有限公司西安分公司，陕西西安 710000
3.
中咨数据有限公司，北京 100083
4.
美国加州交通厅养护处，加利福尼亚萨克拉门托 95835

基金项目:

国家重点基础研究发展计划项目 2018YFB010510401

陕西自然科学基础研究计划项目 2013JQ8017

交通部基础应用项目 2015319812060

中央高校基本科研业务费专项资金项目 310824163202

中央高校基本科研业务费专项资金项目 300102248403

详细信息

作者简介:
徐志刚(1979-), 男, 湖北鄂州人, 长安大学教授, 工学博士, 从事智能交通系统研究

中图分类号: U418.6
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出版历程
- 收稿日期: 2018-08-22
- 刊出日期: 2019-02-25

Research progress on automatic image processing technology for pavement distress

1.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Shijiazhuang Huayan Transportation Technology Co., Ltd., Xi'an Branch, Xi'an 710000, Shaanxi, China
3.
Zhongzi Data Co., Ltd., Beijing 100083, China
4.
Division of Maintenance, California Department of Transportation, Sacramento 95835, California, USA

More Information

Author Bio:
XU Zhi-gang(1979-), male, professor, PhD, xuzhigang@chd.edu.cn

摘要

摘要: 总结了路面破损图像自动处理技术的重要研究成果, 分析了该领域关键技术的研究进展, 包括路面破损检测系统、图像处理算法和识别算法评估; 比较了不同路面破损检测系统与目标自动识别算法的检测精度和适用性, 给出了路面破损图像自动处理技术未来可能的主要研究方向。研究结果表明: 在路面破损检测系统方面, 从早期基于摄影技术的图像采集到目前的3D激光扫描技术, 路面图像采集技术更加便捷和高效, 但破损图像自动分析和目标自动识别算法仍然存在挑战; 在路面破损图像处理算法方面, 传统的路面裂缝目标分割算法已由过去的基于单一特征(灰度、边缘形状等) 的检测方法演化到多特征融合检测方法和图优化检测方法, 还出现了一些精细化的裂缝目标连接与恢复算法, 大幅提高了裂缝检测精度, 但需要的计算资源和人工先验知识库也随之不断增大; 在路面裂缝处理算法评估和比较方面, 主要利用人工分割来评价自动识别结果, 目前迫切需要建立一个面向全球开放的大型路面破损图像数据库, 以客观、有效地评估现有各种路面破损图像处理算法; 基于2D图像特征分析的路面破损图像自动识别算法很难在识别精确性、算法通用性和实时性方面同时取得最佳效果; 近年来, 大量学者开始尝试借助深度学习神经网络自动识别路面破损, 但该技术仍处于活跃的演进过程中; 在提高路面破损自动识别精度和效率方面, 3D激光扫描技术和基于人工智能的深度学习技术的发展将对未来路面破损图像自动识别技术的最终突破产生重大推进作用。
- 路面养护 /
- 路面破损 /
- 图像处理 /
- 破损识别 /
- 裂缝检测 /
- 破损分类
Abstract: The important research achievements on the automatic image processing technology for pavement distress were summarized. The research progress of key technologies in this field was analyzed, including the pavement distress detection system, image processing algorithm and evaluation of recognition algorithm. The detection accuracy and applicability were compared for the different pavement distress detection systems and target automatic recognition algorithms. The possible future research directions of automatic pavement distress image processing technology were presented. Research result shows that in the aspect of pavement distress detection system, from early image acquisition based on the photography technology to the current 3D laser scanning technology, the pavement image acquisition technology becomes more and more convenient and effective. However, there still exist some challenges in the automatic analysis on distress images and automatic recognition algorithm on targets. In the aspect of pavement distress image processing algorithm, the traditional algorithms of segmenting pavement distress targets evolve from the methods using single feature (such as grayscale and edge shape) to multi-feature fusion-based methods and graph optimization-based detection methods. Furthermore, there emerges some dedicated algorithms for recovering or connecting cracks, greatly improving the detection accuracy of crack recognition. Nonetheless, as the complexity of these algorithms grows up, the required computational resources and the size of prior knowledge base both sharply increase. In the aspect of evaluation and comparison of crack processing algorithms, manual segmentation is mainly used to evaluate automatic recognition results. At present, it is urgent to establish a large-scale pavement distress image database opening to the world, so as to objectively and effectively evaluate various existing image processing algorithms for pavement distress. Automatic image processing algorithms for pavement distress based on 2D image features analysis is difficult to achieve the best results with detection accuracy, algorithm versatility and real-time performance simultaneously. In recent years, a large number of scholars begin to use the deep learning neural network to automatically recognize pavement distress, but the technology is still in an active evolution process. In the aspect of improving the accuracy and efficiency of automatic recognition for pavement distress, the 3D laser scanning technology and the deep learning technology based on artificial intelligence will greatly promote the final breakthrough on automatic image recognition technology for pavement distress in the future.
- pavement maintenance /
- pavement distress /
- image processing /
- distress recognition /
- crack detection /
- distress classification

HTML全文

图 1 激光线扫描成像系统

Figure 1. Laser linear scanning imaging system

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图 2 基于红外热成像的路面破损图像采集与处理

Figure 2. Image acquisition and processing of pavement distress based on infrared thermal imaging

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图 3 采用3D激光扫描技术采集的路面破损图像

Figure 3. Pavement distress image collected by 3D laser scanning technology

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图 4 增强的路面图像

Figure 4. Enhanced pavement images

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图 5 采用不同方法对路面裂缝图像进行对比增强的结果

Figure 5. Results of contrast enhancement of pavement crack images using different methods

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图 6 阈值分割结果

Figure 6. Threshold segmentation results

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图 7 受光照和阴影影响的示例图像

Figure 7. Example images affected by light and shade

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图 8 GCA方法处理结果

Figure 8. Processing results of GCA method

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图 9 多尺度检测方法检测结果

Figure 9. Detection results of multi-scale detection method

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图 10 采用直方图估计和形状分析算法识别路面裂缝的结果

Figure 10. Recognition results of pavement crack using histogram estimation and shape analysis algorithm

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图 11 最短路径裂缝检测算法处理结果

Figure 11. Processing resutusing crack detection algorithm with shortest path

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图 12 基于CNN的混凝土路面图像检测流程

Figure 12. Image detection flow of concrete pavement based on CNN

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图 13 CNN总体框架

Figure 13. Overall architecture of CNN

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图 14 强光照下的微弱裂缝

Figure 14. Thin cracks with strong light

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图 15 裂缝检测结果

Figure 15. Crack detection result

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图 16 针对不同尺寸的标准差提出的片段连接方法的自适应机制

Figure 16. Adaptive mechanism of fragment connection methods for standard deviation with different sizes

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图 17 专家系统

Figure 17. Expert system

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图 18 基于FSVM的路面破损分类流程

Figure 18. Flow of pavement distress classification based on FSVM

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图 19 沥青路面中被检测出的坑槽

Figure 19. Detected pits in asphalt pavement

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图 20 Radopoulou所提算法检测的坑槽与补丁

Figure 20. Pit and patch detected by algorithm proposed by Radopoulou

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表 1 六种分割方法的部分评估结果

Table 1. Partial evaluation results of six segmentation methods

方法	不同检测对象的分值/%
方法	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
人工分割	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
动态优化	82	95	98	92	97	85	92	96	87	97	98	98	98	99	98
Canny边缘检测	0	0	7	10	8	0	73	46	53	48	60	58	52	82	51
阈值统计	42	34	40	51	9	54	0	74	48	19	71	76	42	58	0
多尺度小波	7	3	4	3	8	80	73	33	29	32	42	30	29	58	46
分支验证	33	86	0	80	0	26	0	84	71	69	0	60	88	94	0

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