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

Abstract

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

CLC Number:

TP391

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.

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