Automatic darkest filament detection (ADFD): a new algorithm for crack extraction on two-dimensional pavement images

Abstract

Pavement condition information is a significant component in pavement management systems. Precise extraction of road degradations particularly cracks is a critical task for surface safety. Manual surveys, which are labor intensive and costly, have induced several researchers to investigate the use of image processing to achieve automated pavement distress ratings. In the context of fine structures extraction, we present in this paper a novel approach for road crack detection under real conditions using several systems installed differently on a vehicle. It is such an automatic and effective approach that relies on both photometric and geometric characteristics of cracks. Based on an edge detection technique to avoid the bad conditions of image acquisition and an examination algorithm to verify the presence of high concentration of cracking pixels, this approach allows in a first step to select pixels that have great probability of belonging to a crack. Indeed, the originality of this approach stems from the proposed way to compute a set of thin filaments connecting the pixels selected at the first step between them. Finally, a post-processing step is applied to refine the obtained result and confirm either the presence or the absence of cracks in the image. Our proposed approach provides very robust and precise results on 2D pavement images in a wide range of situations and in a fully unsupervised manner. Furthermore, its innovative aspect is reflected in its ability to analyze easily both 2D and 3D pavement images.