Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 × 2448, collected by a lowcost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.
Zhang et. al. REF applied deep convolutional neural network for road crack detection from images collected using a low cost smart phone.
Road crack detection using deep convolutional neural network
2016 IEEE International Conference on Image Processing (ICIP)
2016 IEEE International Conference on Image Processing (ICIP)
Computer Science Engineering