Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing
Pranjal A. Chitale, Kaustubh Y. Kekre, Hrishikesh Shenai, Ruhina Karani, Jay Gala
Abstract
The world is advancing towards an autonomous environment at a great pace and it has become a need of an hour, especially during the current pandemic situation. The pandemic has hindered the functioning of many sectors, one of them being Road development and maintenance. Creating a safe working environment for workers is a major concern of road maintenance during such difficult times. This can be achieved to some extent with the help of an autonomous system that will aim at reducing human dependency. In this paper, one of such systems, a pothole detection and dimension estimation, is proposed. The proposed system uses a Deep Learning based algorithm YOLO (You Only Look Once) for pothole detection. Further, an image processing based triangular similarity measure is used for pothole dimension estimation. The proposed system provides reasonably accurate results of both pothole detection and dimension estimation. The proposed system also helps in reducing the time required for road maintenance. The system uses a custom made dataset consisting of images of water-logged and dry potholes of various shapes and sizes.