Asphalt Pavement Potholes Localization and Segmentation using Deep RetinaNet and Conditional Random Fields

The main aspect of maintaining the roads and highways' durability long life is to detect potholes restore them. A huge number accidents occur on highways due pothole. It also causes financial loss vehicle owners by damaging wheel flat tire. For strategies road management system ITS (Intelligent Transportation System) service, it one major tasks quickly precisely potholes. To solve this problem, we have proposed a deep learning methodology automatically segment pothole region within asphalt pavement images. detection challenging task because arbitrary shape complex structure In our methodology, accurately region, used RetinaNet that creates bounding box around multiple regions. segmentation Conditional Random Field segments detected regions obtained from RetinaNet. There are three steps in image preprocessing, Pothole localization, segmentation. Our results show images were correctly localized with best accuracy 93.04%. Fields (CRF) good results.