Autonomous seam recognition and feature extraction for multi-pass welding based on laser stripe edge guidance network

In this paper, an autonomous seam recognition and feature extraction method for multi-pass welding based on laser stripe edge guidance network is proposed to overcome the interference of strong reflection, spatter, and arc noise in actual welding environment. Firstly, the laser stripe edge guidance network consisting of modified VGGnet, progressive laser stripe feature extraction, non-local laser stripe edge feature extraction, one-to-one guidance module, and multi-feature fusion module is introduced to recognize the laser stripe under heavy arc noises. Afterwards, the gray centroid method is adopted to obtain the thinning laser stripe. Aiming at extracting the position of feature points, the least square method and non-uniform rational B-splines with second derivative are utilized. Finally, experiments and analysis show that our proposed method performs favorable in terms of effectiveness, flexible, accuracy, and robustness, which could meet the actual welding requirements. Besides, the maximum error and maximum root mean square error for feature extraction are 4.7 pixel and 1.78 pixel, respectively.