基于稀疏表示和多特征融合的路面裂缝检测  

Pavement Crack Detection Based on Sparse Representation and Multi-feature Fusion

作　　者：张玉雪 唐振民[1] 钱彬[1] 徐威[1] ZHANG Yu-xue;TANG Zhen-min;QIAN Bin;XU Wei(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094

出　　处：《计算机科学》2018年第7期271-277,共7页Computer Science

基　　金：中国博士后科学基金(2014M551599);国家军口核高基"×××软件支撑平台"(2015ZX01041101)资助

摘　　要：为了提高在实际复杂背景噪声下对路面裂缝检测的精度,提出了一种基于稀疏表示和多特征融合的路面裂缝检测改进算法。该算法首先以图像子块为单位,提取对裂缝识别有效的统计、纹理和形状特征。然后,分别在各个特征矩阵下利用稀疏表示分类方法实现对裂缝子块的识别,再融合不同特征下的识别结果,设计综合识别分类器进行子块检测。最后,在识别出的裂缝子块上,采用基于视觉显著性的像素级检测方法精确提取裂缝细节。在实际高速公路路面数据库上的实验结果表明,该算法有效地提升了路面裂缝检测的精度,具有良好的鲁棒性。In order to improve the performance of the practical pavement crack detection under complex background noise,an improved pavement crack detection algorithm based on sparse representation and multi-feature fusion was proposed.Firstly,this algorithm takes image sub-block as unit,and extracts statistics,texture and shape features which are effective for crack re-cognition.Then,the sparse representation classification method is adopted to realize sub-block recognition under each feature matrix separately,and a comprehensive recognition classifier for sub-block detection is designed by fusing the recognition results under different features.Finally,on the detected sub-block,apixel-level detection method based on visual saliency is used to extract crack details accurately.The experiment results on highway pavement datasets show that the proposed algorithm can effectively improve the accuracy of pavement crack detection and has good robustness.

关 键 词：裂缝检测 稀疏表示 多特征融合 视觉显著性 像素级检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]