基于空间聚集特征的沥青路面裂缝检测方法

张德津; 李清泉; 陈颖; 曹民; 何莉

doi:10.16383/j.aas.2016.c150511

基于空间聚集特征的沥青路面裂缝检测方法

doi: 10.16383/j.aas.2016.c150511

张德津^1,2,,
李清泉^1,2,,
陈颖^3,,
曹民^3,,
何莉^4, ,

1.
深圳大学海岸带地理环境监测国家测绘地理信息局重点实验室深圳 518060
2.
空间信息智能感知与服务深圳市重点实验室深圳 518060
3.
武汉武大卓越科技有限责任公司武汉 430223
4.
湖北工业大学电气与电子工程学院武汉 430068

基金项目:

深圳市战略性新兴产业发展重大技术研究开发项目 JSGG20121026111056204

国家高技术研究发展计划(863计划) 2012AA112503

详细信息

作者简介:
张德津深圳大学博士后.主要研究方向为智能交通系统, 公路无损检测和激光测量技术.E-mail:djzhang@whu.edu.cn

李清泉武汉大学和深圳大学教授.主要研究方向为摄影测量与遥感, 地理信息系统, 智能交通与道路检测.E-mail:liqq@szu.edu.cn

陈颖 2013年获得西安交通大学电子与信息工程学院硕士学位.主要研究方向为图像及视频处理, 模式识别及智能监控.E-mail:chenying_xjtu@126.com

曹民测量遥感高级工程师.武汉大学获学士学位.主要研究方向为电力自动化和智能交通系统.E-mail:13307100949@189.cn

通讯作者:
何莉湖北工业大学副教授.2007年获得华中科技大学博士学位.主要研究方向为人工智能, 数据建模及优化算法.本文通信作者.E-mail:heli.edu@hotmail.com

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出版历程
- 收稿日期: 2015-08-12
- 录用日期: 2015-11-09
- 刊出日期: 2016-03-01

Asphalt Pavement Crack Detection Based on Spatial Clustering Feature

ZHANG De-Jin^{1,2
,},
LI Qing-Quan^{1,2
,},
CHEN Ying^3
,,
CAO Min^3
,,
HE Li^{4
, ,}

1.
Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060
2.
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen 518060
3.
Wuhan Wuda Zoyon Science and Technology Co. Ltd., Wuhan 430223
4.
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068

Funds:

the Major Technology Research Grant for Shenzhen Strategic Emerging Industries JSGG20121026111056204

National High Technology Research and Development Program of China (863 Program) 2012AA112503

More Information

Author Bio:
Postdoctor at Shenzhen University. His research interest covers intelligent transportation system, pavement nondestructive testing, and laser measurement technology

Professor at Wuhan University and Shenzhen University. His research interest covers photogrammetry and remote sensing, geographic information system, and intelligent transportation system and road surface checking

Received her master degree from the School of Electronic and Information Engineering, Xi0an Jiaotong University in 2013. Her research interest covers image and video processing, pattern recognition, and intelligent surveillance

Senior engineer in photogrammetry and remote sensing. He received his bachelor degree from Wuhan University. His research interest covers electric power automation and intelligent transportation system

Corresponding author: HE Li Associate professor at Hubei University of Technology. She received her Ph. D. degree from Huazhong University of Science and Technology in 2007. Her research interest covers artificial intelligence, data modeling, and optimal algorithm. Corresponding author of this paper

摘要

摘要: 沥青路面裂缝自动检测是制约公路养护科学决策的最主要瓶颈.针对现有裂缝检测算法在大规模应用特别是广地域、多路况等复杂环境下算法稳定性、可靠性及实时性等方面存在严重不足问题.本文在观察大量实际工程路面图像基础上, 对路面裂缝特征进行全新定义, 提出了一种基于空间聚集特征的沥青路面裂缝检测方法, 参考裂缝的空间分布、灰度、几何等特征, 以子块图像为处理单元, 采用逐步求精的策略对子块图像进行分割, 快速定位空间聚集区域, 再对聚集区域进行评估得到信度高的裂缝候选区域; 最后以裂缝候选区域为种子区域, 在准确估算裂缝发展趋势的基础上, 结合裂缝片段聚集及相似性等特性, 去除噪声同时合并连接断裂的裂缝, 实现了裂缝区域较为完整的检测.通过测试多路况、多采集环境下近万样本, 并采用不同的方法对测试结果进行评估, 结果显示, 算法对不同类型路面图像中具有不同特征的裂缝区域均具有良好的检测性能, 裂缝定位准确性达到95%以上, 裂缝区域检测的完整性达到90%以上.
- 沥青路面 /
- 裂缝 /
- 聚集区域 /
- 候选区域 /
- 发展趋势
Abstract: Asphalt pavement crack detection is the main bottleneck of advanced decision support for road maintenance. A common problem associated with existing algorithms is lack of stability, reliability and timeliness in large-scale applications, especially in wide geographical areas and varying road conditions. In this paper, a new description of the cracks is proposed by observing a large number of pavement images. And an asphalt pavement crack detection using spatial clustering feature is proposed. Regions of aggregation are the primary targets segmented from original image via a coarse-to-fine methodology which comprehensively takes the spatial distribution, intensities and geometric features of cracks into account. Then candidate crack regions with sufficient confidence are extracted from regions of aggregation. Moreover, a new region growing algorithm is presented on the basis of an accurate estimate of cracks trend, which guarantees the accomplishment of providing complete cracks by merging operation for those highly similar regions and simultaneously eliminating distinct ones. A larger number of images of pavement surface have been taken for experiments, which cover a wide range of different road conditions and varying data collecting environments. The detection results show that our algorithm is satisfactory for a variety of different cracks. The detection accuracy is over 95% and more than 90% of coherent cracks without disconnected fragments have been correctly detected as the integrated ones.
- Asphalt pavement /
- crack /
- clustering region /
- candidate region /
- growth direction

HTML全文

图 1 灰度校正结果说明

Fig. 1 Illustration of the intensity correction result

下载: 全尺寸图片幻灯片

图 2 聚集区域提取过程

Fig. 2 Explanation of the clustering region extraction process

下载: 全尺寸图片幻灯片

图 3 候选区域定位

Fig. 3 Demonstration of the CCRs selection procedure

下载: 全尺寸图片幻灯片

图 4 置信区域方向获取方法

Fig. 4 Illustration of different orientations for region growth

下载: 全尺寸图片幻灯片

图 5 裂缝生长及去噪效果

Fig. 5 Examples of the proposed region growing process

下载: 全尺寸图片幻灯片

图 6 从左到右分别为原始图像、人工描点、本文算法、CrackTree、VCrack、WeightedCrack检测效果

Fig. 6 The performance for some of the representative crack examples with distinctive characteristics (From top to bottom are pavement images, manually detected results, the proposed detection algorithm results, CrackTree detection algorithm results, VCrack detection algorithm results, and WeightedCrack detection algorithm results.)

下载: 全尺寸图片幻灯片

图 7 四种方法三个指标的比较

Fig. 7 The comparison of the FNR, FPR, PPR of Proposed, CrackTree, VCrack, and WeightedCrack methods

下载: 全尺寸图片幻灯片

图 8 裂缝检测完整性评估结果

Fig. 8 Performance evaluation of the completeness of the detected cracks

下载: 全尺寸图片幻灯片

表 1 不同1/p对检测结果的影响

Table 1 The influence of different values of 1/p over detection results

工程名	图片大小	图片数量	1/p	漏检率(%)	误检率(%)
			0.10	5.12	7.83
			0.12	5.03	8.01
工程1	2048×2048	3320	0.14	4.87	8.13
			0.16	4.84	8.19
			0.18	4.82	8.22
			0.20	4.82	8.31
			0.10	4.54	6.25
			0.12	4.49	6.28
工程2	2048×2048	7648	0.14	4.42	6.29
			0.16	4.39	6.33
			0.18	4.38	6.35
			0.20	4.38	6.41

下载: 导出CSV

表 2 路面图像分类结果统计表

Table 2 Statistics for accuracy of crack localization

DB	Method	P	N	TP	TN	FN	FP	FNR(%)	FPR(%)	PPR(%)
	Proposed			1255	1180	30	50	2.3	3.8	96.8
DB1	CrackTree	1285	1230	1230	1158	55	72	4.3	5.5	95.0
	VCrack			1227	1163	58	67	4.5	5.2	95.0
	WeightedCrack			1238	1171	47	59	3.6	4.5	95.8
	Proposed			344	632	8	32	2.3	8.5	96.1
DB2	CrackTree	352	664	337	627	15	37	4.3	10.0	94.9
	VCrack			331	625	21	39	6.0	10.5	94.1
	WeightedCrack			340	626	12	38	3.4	10.0	95.1
	Proposed			540	0	0	0	0	0	100
DB3	CrackTree	540	0	527	0	13	0	2.4	0	97.6
	VCrack			531	0	9	0	1.7	0	98.3
	WeightedCrack			531	0	9	0	1.7	0	98.3
	Proposed			2351	90	30	9	1.3	0.4	98.4
DB4	CrackTree	2381	99	2316	83	65	16	2.7	0.7	96.7
	VCrack			2298	84	83	17	3.5	0.7	96.0
	WeightedCrack			2319	87	62	12	2.6	0.5	97.0
	Proposed			1373	90	10	7	0.7	0.5	98.8
DB5	CrackTree	1383	97	1352	88	31	9	2.2	0.7	97.3
	VCrack			1330	90	53	7	3.8	0.5	96.0
	WeightedCrack			1368	92	15	5	1.1	0.4	98.6
	Proposed			426	1560	6	8	1.4	1.8	99.3
DB6	CrackTree	432	1568	387	1482	45	86	10.4	18.2	93.5
	VCrack			384	1493	48	75	11.1	16.3	93.9
	WeightedCrack			411	1538	21	30	4.9	6.8	97.5

下载: 导出CSV

参考文献(24)

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基于空间聚集特征的沥青路面裂缝检测方法

doi: 10.16383/j.aas.2016.c150511

通讯作者:
何莉湖北工业大学副教授.2007年获得华中科技大学博士学位.主要研究方向为人工智能, 数据建模及优化算法.本文通信作者.E-mail:heli.edu@hotmail.com

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Asphalt Pavement Crack Detection Based on Spatial Clustering Feature

Corresponding author: HE Li Associate professor at Hubei University of Technology. She received her Ph. D. degree from Huazhong University of Science and Technology in 2007. Her research interest covers artificial intelligence, data modeling, and optimal algorithm. Corresponding author of this paper

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基于空间聚集特征的沥青路面裂缝检测方法

doi: 10.16383/j.aas.2016.c150511

通讯作者: 何莉 湖北工业大学副教授.2007年获得华中科技大学博士学位.主要研究方向为人工智能, 数据建模及优化算法.本文通信作者.E-mail:heli.edu@hotmail.com

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Asphalt Pavement Crack Detection Based on Spatial Clustering Feature

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出版历程

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通讯作者:
何莉湖北工业大学副教授.2007年获得华中科技大学博士学位.主要研究方向为人工智能, 数据建模及优化算法.本文通信作者.E-mail:heli.edu@hotmail.com