Abstract
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.
Highlights
1.1 MotivationPavement cracks are common damages on pavement surfaces, which are signed for potential damages in the supporting structures (Lee, 1991)
Traditional road crack detection usually relies on human inspection, limiting the accuracy and efficiency of the measurement (Li et al, 2019)
The data used in this paper is 3D point cloud data obtained from an Mobile laser scanning (MLS) system
Summary
Pavement cracks are common damages on pavement surfaces, which are signed for potential damages in the supporting structures (Lee, 1991). Road surface defects may cause severe troubles in traffic, such as congestion, delay, and even safety problems. It is important to prevent and repair early cracks in the pavement. Traditional road crack detection usually relies on human inspection, limiting the accuracy and efficiency of the measurement (Li et al, 2019). Most of the common practice in the road is usually time-consuming, dangerous, labour-intensive, and subjective. It is a trend to replace traditional crack detection methods with automated or semi-automated ones. Semi-automated methods combine human intervention and machine, while automated methods require minimal human assistance. Automated and semi-automated technologies make it possible to develop real-time pavement distress detection
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