CHBS-Net: 3D Point Cloud Segmentation Network with Key Feature Guidance for Circular Hole Boundaries

Abstract

In laser scanning inspection systems for sheet metal parts, the rapid and accurate inspection of the high-precision holes is not only crucial but difficult. The accuracy of the circular holes, especially the locating datum holes on the parts, plays an important role in the assembly quality. However, accurately segmenting the circular hole boundary points required for circular hole fitting from large-scale scanning point cloud data remains one of the most difficult tasks for inspection accuracy improvement. To address this problem, a segmentation network called the circular hole boundary segmentation network (CHBS-Net) is proposed for boundary point cloud extraction. Firstly, an encoding–decoding–attention (EDA) fusion guidance mechanism is used to address the imbalance in data distribution due to the small proportion of boundary points in the overall point cloud. Secondly, a long short-term memory (LSTM) network parallel structure is used to capture the contour continuity and temporal relationships among boundary points. Finally, the interference of neighboring points and noise is reduced by extracting features in the multi-scale neighborhood. Experiments were performed using real cases from a sheet metal parts dataset to illustrate the procedures. The results showed that the proposed method achieves better performance than the benchmark state-of-the-art methods. The circular hole inspection accuracy is effectively improved by enhancing the segmentation accuracy of the scanning boundary points.

Keywords

• sheet metal part
• point cloud segmentation
• 3D measurement
• deep learning