基于稀疏表示和多特征融合的路面裂缝检测

doi:10.11896／j.issn.1002-137X.2018.07.047

摘要/Abstract

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。

关键词: 多特征融合, 裂缝检测, 视觉显著性, 稀疏表示, 像素级检测

Abstract: 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 re-cognition 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,a pixel-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.

Key words: Crack detection, Multi-feature fusion, Pixel-level detection, Sparse representation, Visual saliency

中图分类号:

TP391

张玉雪,唐振民,钱彬,徐威. 基于稀疏表示和多特征融合的路面裂缝检测[J]. 计算机科学, 2018, 45(7): 271-277. https://doi.org/10.11896／j.issn.1002-137X.2018.07.047

ZHANG Yu-xue,TANG Zhen-min ,QIAN Bin ,XU Wei. Pavement Crack Detection Based on Sparse Representation and Multi-feature Fusion[J]. Computer Science, 2018, 45(7): 271-277. https://doi.org/10.11896／j.issn.1002-137X.2018.07.047

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