武汉工程大学学报2026，Vol.48Issue(2)：209-215,7.DOI:10.19843/j.cnki.CN42-1779/TQ.202505007
基于语义分割网络的点云焊缝识别研究
Weld seam recognition from point cloud using a semantic segmentation network
摘要
Abstract
Teaching-free welding for complex components offers advantages such as a high degree of automation and significant reduction in labor costs.However,its application heavily relies on weld seam recognition methods with strong adaptability and robustness.Most existing approaches exhibit low recognition success rates in multi-workpiece setups or harsh environment.In this paper,we proposed a weld seam recognition method based on a semantic segmentation network using point cloud data.In this method,a camera acquired grayscale images and 3D point clouds of the workpiece;a DeepLabv3-ResNet50 semantic segmentation network was then trained on these grayscale images to extract the weld plane.During deployment,the trained model predicted the pixel coordinates of the planes adjacent to the weld seam,which were subsequently back-projected into the 3D point cloud.A plane intersection algorithm was applied to compute the weld seam segments from the point cloud.Finally,coordinate transformation was used to locate these segments in the robot coordinate system.Experimental results showed that generated trajectory has an average deviation of 0.208 mm from the manually taught trajectory,achieving a recognition accuracy of 95.3%.Compared with the traditional point cloud curvature sampling and octree-based sampling methods,this proposed approach exhibits superior robustness when dealing with multi-plane workpieces and highly reflective environments,demonstrating strong adaptability across varied working conditions.关键词
焊缝识别/语义分割网络/点云处理/机器人路径规划Key words
welding seam recognition/semantic segmentation network/point cloud processing/robot path planning分类
信息技术与安全科学引用本文复制引用
黄昆涛,邹成,周耀胜,陈绪兵,刘希..基于语义分割网络的点云焊缝识别研究[J].武汉工程大学学报,2026,48(2):209-215,7.基金项目
武汉工程大学研究生教育创新基金(CX2024415) （CX2024415）
