A comprehensive method for precisely extracting weld seam feature point in multi-interference environments

Abstract In actual welding environments, factors such as the welding process, light intensity, material properties of the workpiece, and surface quality introduce interferences such as noise, reflection, curve around, fume, and splash in laser images, which degrade the quality of centerline extraction and the precision of weld seam feature point identification. To address these challenges, a comprehensive method for precisely extracting weld seam feature point in multi-interference environments is proposed. Initially, an adaptive thresholding method based on the minimum error prior values (AT-MEPV) is proposed for image binarization to separate laser stripe information from background noise. Subsequently, a novel method for laser stripe centerline extraction is proposed, combining the grayscale centroid method with the Levenberg–Marquardt dual-threshold center point prediction method, to accurately extract the centerline of the laser stripe. Finally, an adaptive iterative random sample consensus algorithm is proposed to extract weld seam feature points precisely. Experimental results demonstrate that the proposed method can effectively extract feature points from various types of weld seams under single and multiple interference conditions, including T-weld, short side T-weld, and V-weld. The average extraction errors for these weld types are 2.1177 pixels, 2.2021 pixels, and 1.4810 pixels, respectively, with corresponding root mean square errors of 0.0544 pixels, 0.0535 pixels, and 0.0711 pixels.