Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.
Pavement crack detection using non‐local theory and iterative sampling