Rui Fan1, Mohammud Bocus2, Yilong Zhu3, Jianhao Jiao4, Li Wang5, Fulong Ma6, Shanshan Cheng5, Ming Liu7
11:00 - 12:30 | Mon 10 Jun | Room 5 | MoAM_P1.11
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.