Road crack detection using deep convolutional neural network and adaptive thresholding

Rui Fan*, Mohammud Junaid Bocus, Yilong Zhu, Jianhao Jiao, Li Wang, Fulong Ma, Shanshan Cheng, Ming Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

20 Citations (Scopus)

Abstract

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, a novel road crack detection algorithm which is based on deep learning and adaptive image segmentation is proposed. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, cracks are extracted from the road surface using an adaptive thresholding method. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Vehicles Symposium, IV 2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages474-479
Number of pages6
ISBN (Electronic)9781728105604
DOIs
Publication statusPublished - Jun 2019
Event30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2019-June

Conference

Conference30th IEEE Intelligent Vehicles Symposium, IV 2019
CountryFrance
CityParis
Period9/06/1912/06/19

Bibliographical note

Funding Information:
This work is supported by grants from the Research Grants Council of the Hong Kong SAR Government, China (No. 11210017 and No. 21202816) awarded to Prof. Ming Liu. This work is also supported by grants from the Shenzhen Science, Technology and Innovation Commission, JCYJ20170818153518789, and National Natural Science Foundation of China (No. 61603376) awarded to Dr. Lujia Wang.

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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