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Crack is one of the most common road distresses which may pose road safetyhazards. Generally, crack detection is performed by either certified inspectorsor structural engineers. This task is, however, time-consuming, subjective andlabor-intensive. In this paper, we propose a novel road crack detectionalgorithm based on deep learning and adaptive image segmentation. Firstly, adeep convolutional neural network is trained to determine whether an imagecontains cracks or not. The images containing cracks are then smoothed usingbilateral filtering, which greatly minimizes the number of noisy pixels.Finally, we utilize an adaptive thresholding method to extract the cracks fromroad surface. The experimental results illustrate that our network can classifyimages with an accuracy of 99.92%, and the cracks can be successfully extractedfrom the images using our proposed thresholding algorithm.

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding