基于张量投票的道路表面裂缝检测

doi:10.3969/j.issn.0255-8297.2015.05.008

应用科学学报 ›› 2015, Vol. 33 ›› Issue (5): 541-549.doi: 10.3969/j.issn.0255-8297.2015.05.008

基于张量投票的道路表面裂缝检测

李爱霞¹, 管海燕², 钟良³, 于永涛⁴

1. 浙江水利水电学院测绘与市政工程学院, 杭州 310018;
2. 南京信息工程大学地理与遥感学院, 南京 210044;
3. 长江委长江空间信息技术工程有限公司, 武汉 430079;
4. 淮阴工学院计算机工程学院, 江苏淮安 223003

收稿日期:2015-04-09 修回日期:2015-08-27 出版日期:2015-09-30 发布日期:2015-09-30
作者简介:李爱霞,博士,讲师,研究方向:摄影测量与遥感、激光扫描数据处理和应用,E-mail:liax_zj@sina.com
基金资助:
浙江省自然科学基金(No.LQ15D010001);浙江省教育厅项目基金(No.Y201432349)资助

Tensor Voting Based Pavement Crack Extraction

LI Ai-xia¹, GUAN Hai-yan², ZHONG Liang³, YU Yong-tao⁴

1. Department of Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China;
2. College of Geography & Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China;
3. Changjiang Spatial Information Technology Engineering Company, Wuhan 430079, China;
4. College of Computer Engineering, Huaiyin Institute of Technology, Huai'an 223003, Jiangsu Province, China

Received:2015-04-09 Revised:2015-08-27 Online:2015-09-30 Published:2015-09-30

摘要/Abstract

摘要： 以车载LiDAR数据为对象,提出一种基于多尺度张量投票技术的道路表面裂缝提取方法. 首先沿行车路线从剖面图中提取道路路坎,通过行车轨迹线约束提取道路数据. 再根据强度和距离信息将道路数据转换成二维特征图像,采用多尺度张量投票法增强特征图像的裂缝信息提取道路表面裂缝. 利用点云数据和道路影像数据进行实验验证,结果表明该方法抗噪能力强,裂缝检测质量高.

关键词: 张量投票, 车载LiDAR数据, 裂缝, 道路特征图像

Abstract: This paper proposes a multi-scale tensor voting framework that applies tensor voting to mobile laser scanning data to extract pavement cracks. Trajectory data are used to extract road curbs from profiles along the travelling line to separate road points from non-road points. The extracted road points are interpolated into road feature images. Thus curvilinear cracks are enhanced and extracted with a multi-scale tensor voting framework. Experiments on mobile laser scanning data and road image data were carried out. The results show that the method is robust to noise in both road images and feature images, and can achieve good performance in pavement crack extraction.

Key words: tensor voting, mobile LiDAR data, pavement cracks, road feature image

中图分类号:

P237

李爱霞, 管海燕, 钟良, 于永涛. 基于张量投票的道路表面裂缝检测[J]. 应用科学学报, 2015, 33(5): 541-549.

LI Ai-xia, GUAN Hai-yan, ZHONG Liang, YU Yong-tao. Tensor Voting Based Pavement Crack Extraction[J]. Journal of Applied Sciences, 2015, 33(5): 541-549.

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基于张量投票的道路表面裂缝检测

Tensor Voting Based Pavement Crack Extraction

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