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.
|Title of host publication||2019 IEEE Intelligent Vehicles Symposium, IV 2019|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|Publication status||Published - Jun 2019|
|Event||30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France|
Duration: 9 Jun 2019 → 12 Jun 2019
|Name||IEEE Intelligent Vehicles Symposium, Proceedings|
|Conference||30th IEEE Intelligent Vehicles Symposium, IV 2019|
|Period||9/06/19 → 12/06/19|
Bibliographical noteFunding 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.
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