Lane Crack Detection Based on Saliency

Abstract

Lane cracks are one of the biggest threats to pavement conditions. The automatic detection of lane cracks can not only assist the evaluation of road quality and quantity but can also be used to develop the best crack repair plan, so as to keep the road level and ensure driving safety. Although cracks can be extracted from pavement images because the gray intensity of crack pixels is lower than the background gray intensity, it is still a challenge to extract continuous and complete cracks from the three-lane images with complex texture, high noise, and uneven illumination. Different from threshold segmentation and edge detection, this study designed a crack detection algorithm with dual positioning. An image-enhancement method based on crack saliency is proposed for the first time. Based on Bayesian probability, the saliency of each pixel judged as a crack is calculated. Then, the Fréchet distance improvement triangle relationship is introduced to determine whether the key point extracted is the fracture endpoint and whether the fast-moving method should be terminated. In addition, a complete remote-sensing process was developed to calculate the length and width of cracks by inverting the squint images collected by mobile phones. A large number of images with different types, noise, illumination, and interference conditions were tested. The average crack extraction accuracy of 89.3%, recall rate of 87.1%, and F1 value of 88.2% showed that the method could detect cracks in pavement well.