This paper presents an approach for defect detection and characterization on aeronautical metallic or composite surfaces, by processing 3D point clouds. For defect detection, we employed deep learning techniques, specifically segmentation models. To obtain a sufficient amount of training data, we developed a custom pipeline for generating synthetic point clouds that represent surfaces with various types of damage. In the testing phase, we used only real point clouds collected using a 3D scanner. For the characterization step, our system aims to provide precise measurements of the geometric properties of the detected defects, such as depth and surface area. For measuring the width and length of the defects, we utilized segmentation masks generated during the detection phase. To address the challenge of measuring defect depth, we relied on traditional computer vision techniques applied to 3D point clouds. We reconstructed an ideal, defect-free surface from points classified as non-defective, and then determined the defect depth as the maximum distance between the defective points and this reconstructed surface.
Džaković et al. (Fri,) studied this question.