Abstract
The integration of laser vision sensors in robotic welding improves seam tracking accuracy, but welding noise poses significant challenges. Our research introduces WeldNet, enhances laser stripe extraction, significantly outperforming traditional and deep neural network (DNN) solutions in efficiency and measurement precision. WeldNet comprises lightweight modules for optimal feature extraction, including Multi-Part Channel Convolution (MPC) blocks, Parallel Shift Multilayer Perceptrons (PS-MLP), and Serial Shift MLP (SS-MLP). A specially designed data augmentation strategy is also integrated to address the complex noise encountered in robotic welding. Experimental results demonstrate WeldNet’s effectiveness in reducing welding noise interference, achieving a real-time processing speed of 145 FPS on RTX 2080 Ti GPU, approximately 5x faster than existing state-of-the-art methods. With a Dice coefficient of 87.52% and an IoU value of 77.82%, WeldNet not only enhances operational efficiency but also markedly improves precision in industrial robotic welding.
Published Version
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