Improved road crack detection based on one-class Parzen density estimation and entropy reduction

Abstract

A novel unsupervised strategy to detect cracks on flexible road pavement images, acquired by laser imaging systems, is proposed. It explores the UINTA entropy reduction filter in an innovative way. A two stage approach is followed, after a pre-processing stage, aimed at reducing the variance of image pixel intensities. First, a one-class clustering, using Parzen density estimation, is applied to select image areas likely to contain cracks, exploiting a simple two dimensional feature space which includes the mean and standard deviation of pixel intensities computed for non-overlapping image blocks. Second, the selected blocks are filtered using the UINTA entropy reduction properties and later automatically labeled as containing cracks, or not. Encouraging experimental crack detection results are presented based on real images captured along Canadian roads.