东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (5): 622-625.DOI: 10.12068/j.issn.1005-3026.2014.05.004

• 信息与控制 • 上一篇    下一篇

基于分区域多尺度分析的路面裂缝检测算法

卢紫微，吴成东，陈东岳，商世博   

  1. (东北大学 信息科学与工程学院， 辽宁 沈阳110819)
  • 收稿日期:2013-07-04 修回日期:2013-07-04 出版日期:2014-05-15 发布日期:2014-08-18
  • 通讯作者: 卢紫微
  • 作者简介:卢紫微(1981-)，女，辽宁海城人，东北大学博士研究生，辽宁石油化工大学讲师;吴成东(1960-)，男，辽宁大连人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61273078，61005032);中央高校基本科研业务费专项资金资助项目(N110604006).

Pavement Crack Detection Algorithm Based on Subregion and Multiscale Analysis

LU Ziwei， WU Chengdong， CHEN Dongyue， SHANG Shibo   

  1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China.
  • Received:2013-07-04 Revised:2013-07-04 Online:2014-05-15 Published:2014-08-18
  • Contact: LU Ziwei
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摘要： 为了提高高速公路路面裂缝检测的准确性，提出一种基于分区域多尺度分析的新型路面缺陷检测算法，从图像的不同尺度上提取裂缝及其周围不同区域的灰度、熵和纹理特征分布信息，获得蕴含方向走势和弯曲程度等参数的特征向量，通过支持向量机(supportvectormachine，SVM)的学习并对所得特征向量进行判断，检测出裂缝点所在位置.实验结果表明，算法与其他路面裂缝检测算法相比，有效地提高了检测的抗噪性、通用性以及准确性，达到了理想的裂缝检测效果，满足公路质检的要求.

关键词: 裂缝检测, 分区域, 多尺度, 方向走势, 弯曲程度

Abstract: In order to improve the accuracy of highway pavement crack detection， a new type of surface defects detection algorithm was proposed based on subregion and multiscale analysis. Gray， entropy and texture features distribution information were extracted in cracks and the surrounding areas from different scales of images. Feature vectors of parameters containing the direction trend and bending degree were acquired， and the crack location was detected through learning the support vector machine (SVM) and judging the eigenvector. Experimental results demonstrated that the resistance to noise， versatility and detection accuracy were improved effectively by the proposed algorithm in comparison to the other pavement cracks detection algorithms. The ideal crack detection effect was achieved， and the requirements of highway quality inspection were met effectively.

Key words: crack detection, subregion, multiscale, direction trend, bending degree

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