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.

本文提出了一种基于激光条纹边缘导引网络的多道次焊缝自主焊缝识别和特征提取方法，以克服实际焊接环境中强反射，飞溅和电弧噪声的干扰。首先，引入了由改进的VGGnet，渐进式激光条纹特征提取，非局部激光条纹边缘特征提取，一对一导航模块和多特征融合模块组成的激光条纹边缘引导网络，以识别下的激光条纹。强烈的电弧声。之后，采用灰色质心法获得细化激光条。针对提取特征点的位置，利用最小二乘法和具有二阶导数的非均匀有理B样条。最后，实验和分析表明，本文提出的方法在有效性，灵活性，准确性和鲁棒性方面表现良好，可以满足实际的焊接要求。此外，特征提取的最大误差和最大均方根误差分别为4.7像素和1.78像素。