Pothole detection and dimension estimation by deep learning

Abstract

Maintenance of roads is a crucial part after the construction of roads in order to improve its design life. Without proper maintenance, deterioration occurs more rapidly out of which potholes are the most common type of road distress that can pose a significant hazard to passengers and vehicles. In order to improve road maintenance, automated systems contribute to improving road safety and reducing infrastructure costs. In this paper one such automated pothole detection system is used by applying CNN (Convolution Neural Network) a deep learning approach with the object detection YOLO (You Only Look Once) to detect potholes in real time. The proposed model used here is trained from scratch on a large pothole dataset with an epochs value of 200, and is validated and tested on custom made dataset. The trained model provided accurate results with an mAP50 of 92% in detection of potholes. Further, an image processing method based on spatial resolution factor is used for dimension estimation of the potholes. The findings of this study assist in the inspection of non-destructive automatic pavement conditions that also contributes in improving road safety and reducing the time and cost required for road maintenance.