计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 616-619.doi: 10.11896/jsjkx.201200059

• 交叉& 应用 • 上一篇    下一篇

基于U-net的道路缺陷检测

彭磊, 张辉   

  1. 长沙理工大学电气与信息工程学院 长沙410000
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 张辉(zhanghuiby@csust.edu.cn)
  • 作者简介:1249028596@qq.com

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.

摘要: 道路是现代交通运输最主要的途径之一,道路缺陷对于道路安全有着巨大威胁。因此准确检测道路缺陷对道路养护修缮具有重要意义。道路缺陷具有低连续性和低对比度的特点,现阶段多采用人工检测方法,检测效率低,人力成本高,且检测人员的安全可能会遭受威胁。随着深度学习的发展,神经网络方法被广泛应用于工程实践。U-net是具有编码器-解码器结构的端到端深度学习模型,对微小对象检测能力强,适用于道路裂缝缺陷检测。利用U-net深度学习网络对道路缺陷进行检测,能提高检测效率,无需人工干预,保证检测人员安全,降低检测的人工成本。实验结果表明,U-net网络在数据集Crack500上的效果优于FCN,Segnet等语义分割网络,在保持较高精度的情况下实现了道路缺陷检测。在此基础上对U-net网络层数进行超参数优化,确定该数据集上的最优U-net网络结构。

关键词: U-net, 卷积神经网络, 缺陷检测, 深度学习

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

中图分类号: 

  • TP391
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