Computer Science ›› 2018, Vol. 45 ›› Issue (7): 271-277.doi: 10.11896/j.issn.1002-137X.2018.07.047

• Graphics, Image & Pattem Recognition • Previous Articles     Next Articles

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

ZHANG Yu-xue,TANG Zhen-min ,QIAN Bin ,XU Wei   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
  • Received:2017-01-21 Online:2018-07-30 Published:2018-07-30

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, Sparse representation, Multi-feature fusion, Visual saliency, Pixel-level detection

CLC Number: 

  • TP391
[1]CHENG H D,MIYOJIM M.Automatic pavement distress detection system [J].Journal of Information Sciences,1998,108(1):219-240.
[2]ZHANG D J,LI Q Q,CHEN Y,et al.Asphalt Pavement Crack Detection Based on Spatial Clustering Feature [J].Acta Automatica Sinica,2016,42(3):443-454.(in Chinese)
[3]QIAN B,TANG Z M,SHEN X B,et al.Pavement crack detection based on multi-feature manifold learning and matrix factorization [J].Chinese Journal of Scientific Instrument,2016,37(7):1639-1646.(in Chinese)
[4]GAO J Z,REN M W,TANG Z M,et al.Automatic road crack detection and identification[J].Computer Engineering,2003,29(2):149-150.(in Chinese)
[5]LI Q,LIU X.Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method[C]∥Congress on Image and Signal Processing.New York:IEEE Press,2008:792-796.
[6]LIU F,XU G,YANG Y,et al.Novel approach to pavementcracking automatic detection based on segment extending[C]∥International Symposium on Knowledge Acquisition and Mode-ling.New York:IEEE Press,2008:610-614.
[7]YAN M D,BO S B,HE Y Y.A method of image detection and analysis for pavement crack based on morphology[J].Journal of Engineering Graphics,2008,29(2):142-147.(in Chinese)
[8]NEJAD F M,ZAKERI H.An optimum feature extraction me-thod based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification[J].Expert Systems with Applications,2011,38(8):9442-9460.
[9]MA C X,ZHAO C X,HU Y,et al.Road crack detection based on NSCT and morphology[J].Journal of Computer-Aided Design & Computer Graphics,2009,21(12):1761-1767.(in Chinese)
[10]XU W,TANG Z M,LV J Y.Pavement crack detection based on image saliency [J].Journal of Image and Graphics,2013,18(1):69-77.(in Chinese)
[11]OLIVEIRA H,CORREIA P L.Automatic road crack detection and characterization[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(1):155-168.
[12]HU Y,ZHAO C,WANG H.Automatic pavement crack detection using texture and shape descriptors[J].Iete Technical Review,2014,27(5):398-405.
[13]QIAN B,TANG Z M,XU W,et al.Pavement crack detection algorithm based on sub-patch discriminant analysis[J].Journal of Image and Graphics,2015,20(12):1652-1663.(in Chinese)
[14]ZHANG L,YANG F,ZHANG Y D,et al.Road crack detection using deep convolutional neural network[C]∥IEEE International Conference on Image Processing.New York:IEEE Press,2016.
[15]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2009,31(2):210-227.
[16]YIN H F,WU X J,CHEN S G.Improved LSRC and its application in face recognition[J].Computer Science,2015,42(8):48-51.(in Chinese)
[17]QIAN B,TANG Z,XU W.Pavement crack detection based on improved tensor voting[C]∥International Conference on Computer Science & Education.2014:397-402.
[18]PERONA P,MALIK J.Scale-space and edge detection using ani-sotropic diffusion [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,12(7):629-639.
[19]HARALICK R M.Texture features for image classification[J].IEEE Transactions on Systems Man & Cybernetics,1975,3(6):610-621.
[20]ZHANG Z,XU Y,YANG J,et al.A Survey of Sparse Representation:Algorithms and Applications[J].IEEE Access,2015,3:490-530.
[21]LEE D D,SEUNG H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(6755):788-791.
[22]CAI D,HE X,HAN J,et al.Graph Regularized Nonnegative Matrix Factorization for Data Representation[J].IEEE Tran-sactions on Pattern Analysis & Machine Intelligence,2011,33(8):1548-1560.
[23]ACHANTA R,HEMAMI S,ESTRADA F,et al.Frequency-tuned salient region detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2009:1597-1604.
[24]CHU X M,WANG R B.Asphalt Pavement Surface Distress Ima-ge Recognition Based on Neural Network[J].Journal of Wuhan University of Technology,2004,28(3):373-376.(in Chinese)
[25]DOYCHEVA K,KOCH C,K NIG M.GPU-Enabled Pavement Distress Image Classification in Real Time[J].Journal of Computing in Civil Engineering,2016,31(3):1943-1952.
[1] TIAN Xu, CHANG Kan, HUANG Sheng, QIN Tuan-fa. Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation [J]. Computer Science, 2020, 47(9): 135-141.
[2] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[3] CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang. Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight [J]. Computer Science, 2020, 47(6A): 181-186.
[4] WU Qing-hong, GAO Xiao-dong. Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine [J]. Computer Science, 2020, 47(6): 121-125.
[5] HU Yu-jia, GAN Wei, ZHU Min. Enhancer-Promoter Interaction Prediction Based on Multi-feature Fusion [J]. Computer Science, 2020, 47(5): 64-71.
[6] LIU Jun-qi,LI Zhi,ZHANG Xue-yang. Review of Maritime Target Detection in Visible Bands of Optical Remote Sensing Images [J]. Computer Science, 2020, 47(3): 116-123.
[7] WANG Hong-xing, CHEN Yu-quan, SHEN Jie, ZHANG Xin, HUANG Xiang, YU Bin. Novel Semi-supervised Extreme Learning Machine and its Application in Anti-vibration HammerCorrosion Detection [J]. Computer Science, 2020, 47(12): 262-266.
[8] LI Xiao-yu,GAO Qing-wei,LU Yi-xiang,SUN Dong. Image Fusion Method Based on Image Energy Adjustment [J]. Computer Science, 2020, 47(1): 153-158.
[9] LI Gui-hui,LI Jin-jiang,FAN Hui. Image Denoising Algorithm Based on Adaptive Matching Pursuit [J]. Computer Science, 2020, 47(1): 176-185.
[10] ZHANG Bing, XIE Cong-hua, LIU Zhe. Multi-focus Image Fusion Based on Latent Sparse Representation and Neighborhood Information [J]. Computer Science, 2019, 46(9): 254-258.
[11] SONG Xiao-xiang,GUO Yan,LI Ning,YU Dong-ping. Missing Data Prediction Algorithm Based on Sparse Bayesian Learning in Coevolving Time Series [J]. Computer Science, 2019, 46(7): 217-223.
[12] JIN Kun, CHEN Shao-chang. Status and Development of Gait Recognition [J]. Computer Science, 2019, 46(6A): 30-34.
[13] WU Fan, LI Shou-shan, ZHOU Guo-dong. Movie Review Professionalism Classification Using LSTM and Features Fusion [J]. Computer Science, 2019, 46(6A): 74-79.
[14] WANG Xiao, ZOU Ze-wei, LI Bo-bo, WANG Jing. Target Detection in Colorful Imaging Sonar Based on Multi-feature Fusion [J]. Computer Science, 2019, 46(6A): 177-181.
[15] ZHANG Fu-wang, YUAN Hui-juan. Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity [J]. Computer Science, 2019, 46(6A): 188-191.
Full text



[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[8] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[9] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .