Pothole Detection with Image Processing and Spectral Clustering
Pothole detection is one of the important tasks for the proper planning of repairs and rehabilitation of the asphalt-surfaced pavements. Pothole repair is necessary in those situations where potholes compromise safety and pavement ride-ability. Existing methods for detection and estimation of potholes usually use sophisticated equipment and impose computationally intensive tasks. In this paper, we present a new unsupervised vision-based method, which does not require expensive equipment, additional filtering and training phase. Our method deploys image processing and spectral clustering for identification and rough estimation of potholes. Spectral clustering is used for identification of regions with histogram-based data from gray-scaled image. Based on these results, we identify potholes and estimate their surface. Method is tested on images with different pothole shapes and the results show that this method estimates potholes with reasonable accuracy. Key–Words: Pothole detection, Unsupervised method, Spectral clustering, Image processing, Image segmentation, Vision-based approach