A Probabilistic Superpixel-Based Method for Road Crack Network Detection

Abstract

This paper presents a probabilistic superpixel-based method for detecting road crack networks. The proposed method includes the techniques of skeletonization and end-growing at the superpixel level, which lend to the extraction of slender crack features from road images. Probabilistic crack pixel refinement is implemented, followed by geometry filters and binary crack cleaning operations, with the end goal of presenting cracks in their simplest form for further high-level characterization. The performance study used to characterize this crack detection algorithm was not constrained by crack type, pavement type, or even image resolution. This approach boasts a median pixel-wise distance error rate of less than one pixel, and for a 100-image dataset, the average detected crack length was within 18% of the ground truth crack length.