An improved graph attention network for semantic segmentation of industrial point clouds in automotive battery sealing nail defect detection

Accurate defect detection in automotive battery sealing nails is vital for safety and reliability. Traditional methods combine two-dimensional (2D) vision for localization with three-dimensional (3D) vision for measurement, resulting in complex workflows and reduced efficiency. We propose Local Graph Attention for Semantic Segmentation (LGASS), an end-to-end 3D point cloud segmentation model. LGASS processes raw point cloud data from structured-light systems, performing simultaneous defect localization and geometric quantification in a single stage. By leveraging a graph attention mechanism in an encoder–decoder architecture, LGASS captures local geometric features and long-range dependencies, excelling on industrial metallic surfaces. Experiments show LGASS achieves 99.47% Overall Accuracy (OA), 92.37% mean Accuracy (mAcc), and 79.23% mean Intersection over Union (mIoU), offering a robust solution for automated sealing nail inspection.