Depth extraction of 3D defects on curved surfaces with multi-line lasers

Abstract

Abstract The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, three-dimensional (3D) defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.