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Automatic Recognition of Pavement Crack via Convolutional Neural Network

  • Shangbing GaoEmail author
  • Zheng Jie
  • Zhigeng Pan
  • Fangzhe Qin
  • Rui Li
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10790)

Abstract

Conventional visual and manual road crack detection method is labor-consuming, non-precise, dangerous, costly and also it can affect transportation. With crack being the main distress in the actual pavement surface, digital image processing has been widely applied to cracking recognition recently. This paper presents the preprocessing method, segmentation method, the locating method, and a novel convolutional neural network based pavement cracking recognition method in the area of image processing. This paper trains and tests aforementioned 5-layer convolutional neural network on the pavement crack dataset. The experimental result shows that this 5-layer convolutional neural network performs better than that classical conventional machine learning method. Actual pavement images are used to verify the performance of this method, and the results show that the surface crack could be identified correctly and automatically. The convolutional neural network can learn the features of crack well and sort these aircraft with a high classification accuracy.

Keywords

Active contour Convolutional neural network Pavement crack 

Notes

Acknowledgment

This work is sponsored by the National Natural Science Foundation of China (NSFC) #61402192, six talent peaks project in Jiangsu Province (XYDXXJS-011, XYDXXJS-012), the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) Grant No. 30916014107, Jiangsu university students’ innovative undertaking projects No. 201611049034Y.

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Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Shangbing Gao
    • 1
    • 2
    Email author
  • Zheng Jie
    • 1
  • Zhigeng Pan
    • 2
  • Fangzhe Qin
    • 1
  • Rui Li
    • 1
  1. 1.The Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Faculty of Computer and Software EngineeringHuaiyin Institute of TechnologyHuai’anPeople’s Republic of China
  2. 2.Virtual Reality and Human-Computer Interaction Research CenterHangzhou Normal UniversityHangzhouChina

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