To achieve precise real-time tracking of weld seams, designing a weld seam tracking system that employs the laser vision sensor is essential. Throughout the welding process, the occurrence of welding noise is inevitable, which subsequently obscures critical weld feature point and diminishes welding accuracy. Consequently, we have proposed an image segmentation method employing a lightweight segmentation network to separate this noise. We first establish its structural design by incorporating the UNet as the initial network and introducing a novel V-layer structure. Then, we employ alternating direction method of multipliers (ADMMs) optimization principles to prune and accelerate the initial network. Furthermore, we have designed a seam tracking system to validate our proposed method. The experiment results, when integrated into the efficient convolution operators for tracking (ECO) algorithm, demonstrate an average localization error for lap-type workpieces is 0.19 mm, for butt-type workpieces, it is 0.053 mm, and the maximum localization error for both types remains within 0.35 mm, fulfilling the precision and real-time performance criteria set by the weld seam tracking system. Ultimately, our proposed lightweight segmentation method is not limited to the welding realm, serving as a valuable reference for segmentation applications in various other industrial fields.

中文翻译：

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结合轻量级分割网络设计和基于 ADMM 的结构化剪枝的机器人焊缝跟踪系统

为了实现焊缝的精确实时跟踪，设计采用激光视觉传感器的焊缝跟踪系统至关重要。在整个焊接过程中，焊接噪声的出现是不可避免的，这会掩盖关键的焊接特征点并降低焊接精度。因此，我们提出了一种采用轻量级分割网络来分离这种噪声的图像分割方法。我们首先通过将 UNet 作为初始网络并引入新颖的 V 层结构来建立其结构设计。然后，我们采用交替方向乘子法（ADMM）优化原理来修剪和加速初始网络。此外，我们设计了一个焊缝跟踪系统来验证我们提出的方法。实验结果表明，当集成到高效卷积算子跟踪（ECO）算法中时，搭接型工件的平均定位误差为0.19 mm，对接型工件的平均定位误差为0.053 mm，并且两者的最大定位误差类型保持在 0.35 毫米以内，满足焊缝跟踪系统设定的精度和实时性能标准。最终，我们提出的轻量级分割方法不仅限于焊接领域，为其他各个工业领域的分割应用提供了有价值的参考。