Cracks area common type of pavement damage.Thus,the accurate and timely detection and evaluation ofthe statusofpavementcracks arecrucialforroad condition monitoring and maintenance decision-making.This study proposes a pavement crack feature extraction method based on image processing.First,we performed a connected-domain analysis on the segmented pavement binary image,whereby the connected domains representing cracks were selected based on the feature differences between the crack and interference regions.Subsequently,we applied an improved fast parallel-thinning algorithm to extract the crack skeleton,and the branch length of the endpointwas used as the standard to remove burrs in the skeleton.To address the problems of skeletal fracture and inward shortening,we used the direction of the endpoints and the distance between them to connectand grow the skeleton.The experimentalresults show that this method can effectively remove interfering regions from the segmentation results,thereby extracting a clear and complete single-pixel skeleton of cracks withoutburrs to betterreflectthe main structuraland morphologicalcharacteristicsofthe crack.