A new approach to detect surface defects from 3D point cloud data with surface normal Gabor filter (SNGF)

Abstract

Surface defect detection is essential feedback for quality control in manufacturing processes. This paper presents a new defect detection method, Surface Normal Gabor Filter (SNGF), to detect surface defects using laser scanning point cloud data. The SNGF method transfers the 3D point cloud data to surface normal vectors to normalize the surface topology geometry. The surface normal vectors are then converted into complex numbers and processed by a Gabor Filter to extract defect-induced geometric features. The feasibility of SNGF is validated in the case of studies for detecting different types of defects on textured surfaces through simulations and experiments. The experimental results show improved stability in detecting defects with different sizes compared to the conventional Region Growing Segmentation Algorithm (RGSA). In addition, test results show the running time of SNGF methods is up to 138 times shorter than the RGSA when processing point cloud data sets with a range of 19,600∼313,600 data points.