Efficient Multiple Seams Detection via CAD-Guided Image Segmentation for Robotic Welding

Robotic welding is a critical component of industrial automation, with weld seam detection playing a vital role in ensuring both accuracy and efficiency in welding operations. While precise laser sensors and point cloud cameras are commonly employed to improve welding detection accuracy, current methods often fall short of meeting the speed requirements essential for industrial applications. This paper proposes an efficient multi-weld seams detection method that leverages CAD-guided image segmentation to achieve fast and accurate detection. Our approach employs computationally efficient image segmentation to detect weld seams, in contrast to conventional point cloud processing methods, which involve heavy computational overhead. The detected weld seams in image space are then mapped to the 3D point cloud, enabling the final robot-executable welding paths. Furthermore, to enhance detection accuracy, we introduce a background removal method guided by the CAD model of the workpiece, providing clean data for the image segmentation process. Extensive experiments conducted on the real-world robotic welding system validate the proposed method, demonstrating significant improvements in both accuracy and efficiency compared to existing state-of-the-art techniques.