Entropy-Driven Adaptive Neighborhood Selection and Fitting for Sub-Millimeter Defect Detection and Quantitative Evaluation in Magnetic Tiles

Abstract

Surface defects in magnetic tiles pose significant challenges to the performance and reliability of permanent magnet motors. Traditional defect detection methods, including visual inspection and 2D imaging, are limited by subjectivity, resolution constraints, and a lack of depth information, making precise defect quantification challenging. To address this challenge, this study explores a defect detection and quantitative evaluation framework based on high-resolution 3D laser scanning technology. Our approach integrates point cloud acquisition with luminance and point cloud mapping (LPM) to enhance defect visualization. Furthermore, we introduce an adaptive neighborhood selection method based on information entropy, enabling accurate normal vector and curvature estimation while reducing reliance on manual parameter tuning. Even when the point cloud density decreases to 40%, the mean estimation error and root-mean-square error remain within 3°. By leveraging single-frame and multi-frame point cloud fitting, our method transitions from coarse defect extraction to fine refinement, enhancing detection precision. To further improve accuracy and minimize false negatives, we apply region-growing techniques for defect region completion. Experimental results indicate that our method can reliably detect surface defects as small as 0.07 mm2, achieving an average precision of 93.91%, a recall of 95.97%, and an F1 of 94.91%. Compared to conventional 2D image-based methods, our method offers superior defect quantification, lower computational costs, and minimal hardware requirements, making it highly suitable for real-time online defect detection in industrial applications.