Three-dimensional pavement crack detection based on primary surface profile innovation optimized dual-phase computing

Abstract

Accurate pavement crack detection has long been a challenging task, causing significant difficulties to the pavement management sectors in the managerial decision making. The high complexity of the crack’s characteristics and the less effective of the crack analytical tools are the two crucial aspects to be accounted for. Recently, three-dimensional (3D) technology based high precision crack detection methodologies has undergone extensive developments. Nevertheless, none of those methods has taken into the errors caused by the data collection systems into consideration, resulting in a less satisfying performance. Hence, the primary objective of this research is to outline the Primary Surface Profile (PSP) optimized dual-phase computing 3D crack detection methodology. Two years ago, variations caused by the automatic 3D data collection systems were observed, so researchers proposed PSP based data filtering algorithm. Therefore, this research is the upgrade solution of the previous innovation regarding the unbiased 3D pavement crack detection. Firstly, the dual-phase computing approach is proposed in dealing with the non-variance 3D data. Then, the self-adaptive 3D PSP generation method is introduced. Finally, PSP is embedded in the dual-phase computing method for performance optimization. For performance assessment, both precisions and recalls of the proposed approach are compared with conventional method for transverse, longitudinal, and map crack detections. Even crack detection precisions are found for both methods, which are all higher than 0.9. However, the recalls of the proposed method (transverse cracks:0.973, longitudinal cracks:0.981, map cracks:0.940) are significantly outperforming non-optimized dual-phase computing method (transverse cracks: 0.682, longitudinal cracks: 0.789, map cracks:0.811).