Data augmentation for fuselage panel inspection via 3D point cloud segmentation

Automated inspection of mechanical assemblies can be significantly improved through the application of deep learning techniques to three-dimensional (3D) point cloud segmentation. In industrial environments, however, the use of such models is often constrained by the limited availability of annotated datasets, caused by the high cost, time demands, and confidentiality of data acquisition. To address these limitations, a fully instance and semantically annotated dataset of airplane fuselage panels collected in laboratory conditions is presented, together with a deep learning approach for detecting and classifying rivets in 3D scans. The proposed method enables the detection of defects such as missing or damaged rivets. To overcome limited training data, an augmentation strategy was deployed, designed to improve the detection of damaged and missing rivets. The influence of different scanning devices and resolutions on inspection accuracy is also evaluated across multiple deep learning models. This whole process was intended to be used as a preparation step in the reverse engineering pipeline.