Abstract

In the seam tracking process based on laser vision, noise interference such as arc and splash makes it difficult to accurately extract a weld location from a welding image. To solve this problem, although many algorithms have been proposed, they could not achieve good tracking effect when confronted with continuous strong noise interference. Therefore, a two-stage seam tracking method named Heatmap method combining welding image inpainting and feature point locating from a heatmap is proposed in this paper. Firstly, a welding image inpainting model is constructed based on adversarial learning strategy, which could effectively resist the strong noise interference. Secondly, a heatmap generating model is built to map the global feature information from a welding image to the heatmap of feature point, and realize the seam feature point locating. Furthermore, by integrating the welding image inpainting model and the heatmap generating model, a robust seam tracking system is constructed. Finally, a series of seam locating comparative tests and welding experiments are carried out. The results show that the average locating error of the proposed Heatmap seam tracking method is within ±0.15 mm, demonstrating that the proposed method could well resist interference of continuous strong noise and further improve the welding accuracy and robustness of a seam tracking system.

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