Automatic Road Surface Defect Detection From Grayscale Images

Video health monitoring of large road networks requires the repeated collection of surface images to detect the defects and their changes over time. Vehicle mounted video equipment can easily collect the data, but the amount of data that can be collected in a single day prohibits interactive or semi-automated processing schemes as they would also not be cost-effective. A new approach that is fully automated to detect road surface defects from large amounts of high-resolution grayscale images is presented. The images are collected with a vehicle-mounted rear-facing 5MP video camera complemented by GPS based positioning information. Our algorithm starts by correcting the images for radial and angular distortion to get a bird's-eye view image. This results in images with known dimensions (consistent in width per pixel) which allow data to be accurately placed on geo-referenced maps. Each of the pixels in the image is labeled as crack or non-crack using a Markov Random Field (MRF) approach. The data used for testing and training are disjoint sets of images collected from the streets of Boston, MA, USA. We compare our road surface defect detection results with other techniques/algorithms described in the literature for accuracy and robustness.