Instance Segmentation of Road Pavement Cracks

Robust automatic pavement crack detection is critical to automated road condition evaluation. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. This study makes literature review of road damage detection issues. The paper considers the existing datasets for detection and segmentation distress of road and asphalt pavement. A CNN for pavement cracks instance segmentation has been developed with the use of images from the driver's seat view. A method for generating a synthetic dataset is also presented, and effectiveness of its applicability to the current problem is evaluated. The relevance of the study is emphasized by research on pixel-level automatic damage detection remains a challenging problem, due to heterogeneous pixel intensity, complex crack topology, poor illumination condition, and noisy texture background.