An improved minimal path selection approach with new strategies for pavement crack segmentation

Abstract

Intelligent pavement detection technology provides a new research idea for identifying pavement cracks. To solve the problem that traditional minimal cost path selection (MPS) is computationally intensive and inefficient in crack segmentation, this study proposes an improved minimal cost path algorithm (IMPS). The contribution of IMPS is reflected in its more efficient path planning than MPS and better choice of source and destination of each path by using new strategies. First, a grayscale pavement image is divided into nonoverlapping cells with a certain size. Next, the darkest points in each cell are selected as candidate points and adjacent candidate points are connected based on IMPS. Then pseudocracks are removed based on postprocessing steps before cracks are well segmented. IMPS is compared with other crack segmentation algorithms to prove its reliability. The efficiency of IMPS is increased by approximately 56% than MPS. The accuracy of IMPS is higher than that of MPS and other methods, with Mean Intersection over Union reaching 73.99%.