Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 616-619.doi: 10.11896/jsjkx.201200059

• Interdiscipline & Application • Previous Articles     Next Articles

U-net for Pavement Crack Detection

PENG Lei, ZHANG Hui   

  1. School of Electrical & Information Engineering,Changsha University of Science & Technology,Changsha 410000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:PENG Lei,born in 1996,postgraduate.His main research interests include image processing and deep learning.
    ZHANG Hui,Ph.D,assistant professor,visiting scholar.His main research interests include machine vision,sparse representation,visual tracking.

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Abstract: Road is one of the most crucial ways for transportation.Crack on road will cause great danger to transportation if you leave it unchecked,so it is important to detect crack precisely in road maintenance.Road cracks are usually discontinuous and low-contrast which is difficult to detect using traditional methods of image processing.In this paper,we utilize U-net for road crack detection which is an end-to-end with encoder-decoder structure efficient deep learning network on dataset Crack500,while traditional methods are time-consuming and labor-consuming.U-net is appropriate for road crack detection because of its ability to catch fine details in image.Experiment results demonstrate that U-net outperforms other detect methods.Furthermore,we discuss the difference when modifying the number of conv-blocks in U-net.Experiment results show that it achieves best performance when the number of conv-blocks set to be 7.

Key words: Convolutional neural network, Deep learning, Defect detection, U-net

CLC Number: 

  • TP391
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