Plane Defect Detection Based on 3D Point Cloud

In the production of industrial products, surface defect detection is mostly carried out through manual inspection. However, this detection method has several shortcomings, such as low efficiency, limited accuracy, and high inspection costs. To address these issues, we design an improved random sampling consistency (RANSAC) algorithm based on adaptive parameters of 3D point cloud data for plane defect detection. The main steps of our algorithm include the down sampling function which contains adaptive parameters, optimized based on KD-tree proximity substitution method. Our algorithm also includes the RANSAC segmentation and fitting plane function of adaptive parameters. Experimental results demonstrate that our algorithm can accurately identify protrusions or indentations defects of 1 mm or larger in those planes based on point clouds data, with a recognition rate more than 90%. The experimental results validate the suitability of our algorithm for industrial applications, offering an efficient and cost-effective solution for plane defect detection.