Laser welding is an indispensable link in most types of industrial production. The realization of welding automation by industrial robots can greatly improve production efficiency. In the research and development of the welding seam tracking system, information on the position of the weld joint needs to be obtained accurately. For laser welding images with strong and complex interference, a weld tracking module was designed to capture real-time images of the weld, and a total of 737, 1920 × 1200 pixel weld images were captured using the device, of which 637 were used to create the dataset, and the other 100 were used as images to test the segmentation success rate. Based on the pixel-level segmentation capability of the semantic segmentation network, this study used an encoder-decoder architecture to design a lightweight network structure and introduced a channel attention mechanism. Compared to ERF-Net, SegNet, and DFA-Net, the network model in this paper has a fast segmentation speed and higher segmentation accuracy, with a success rate of 96% and remarkable segmentation results.
Keywords: deep learning; laser welding; seam tracking; semantic segmentation.