Contour detection and salient feature line regularization for printed circuit board in point clouds based on geometric primitives

Abstract

Effective extraction of the printed circuit board (PCB)’s key contour feature line is of great significance in the optoelectronic industry, especially for defect detection and accurate dimensional measurement. Here, we develop a method to convert the fold edge points into boundary points and then extract the salient contour feature lines of the PCB. The method is performed directly on the real PCB point cloud, and no image processing or 3D reconstruction is required. Our approach involves three key steps: point cloud preprocessing, feature point detection, and contour feature line regularization. Firstly, the PCB point cloud’s preprocessing is completed using the k-d tree and pass-through filtering algorithms. Secondly, through the least-median-of-squares (LMS) and the Euclidean-Clustering-Based-on-Gaussian-Mapping (ECGM), the fold edge points are converted into boundary points. Then, the contour feature points are identified by angle criterion between lines formed by the query point with respect to its neighboring points. Finally, the feature points are regularized by the self-pruning B-spline curve based on squared distance minimization (SDM). Compared with the other methods, our approach extracts more salient contour points and fewer redundant points. Experiments on real test data also suggest that this method has good potential in extracting feature lines, which ensures accuracy and strong applicability.