Coarse-to-Fine Detection of Multiple Seams for Robotic Welding
<|reference_start|>Coarse-to-Fine Detection of Multiple Seams for Robotic Welding: Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The performance of the proposed method has been comprehensively tested on various workpieces featuring both linear and curved weld seams and in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method's efficiency and effectiveness. Videos of the real-world experiments can be found at https://youtu.be/pq162HSP2D4.<|reference_end|>
@article{wei2024coarse-to-fine,
    title={Coarse-to-Fine Detection of Multiple Seams for Robotic Welding},
    author={Pengkun Wei, Shuo Cheng, Dayou Li, Ran Song, Yipeng Zhang, and Wei
  Zhang},
    journal={arXiv preprint arXiv:2408.10710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10710},
    primaryClass={cs.CV cs.AI}
}