Multi-layer multi-pass weld seam information extraction based on residual network and conditional generative adversarial network

In multi-layer multi-pass welding, cumulative molten pool deposition, arc radiation interference, and thermal deformation degrade groove boundary discernibility, rendering conventional machine vision methods inadequate for high-precision seam extraction. To address this, we propose a framework integrating residual network and conditional generative adversarial network integrating laser vision sensing with deep adversarial learning. The model leverages residual network's residual blocks for multi-scale feature retention and conditional generative adversarial network's adversarial training for photorealistic feature map generation. A Color-Attention mechanism dynamically amplifies spectral sensitivity to laser stripes while suppressing arc noise, and an auxiliary centroid loss enforces spatial-geometric consistency between synthesized and actual seams. Experimental results demonstrate that compared to state-of-the-art methods, the proposed method achieves an 80% reduction in Mean Absolute Error(MAE), It also attains a feature extraction rate of 36.72 frames per second (FPS) and keeps error fluctuations below 0.6 pixels under multi-pass superposition conditions. This study establishes a groundbreaking spectral-geometric constrained adversarial framework that comprehensively addresses two critical challenges in multi-layer multi-pass (MLMP) welding: the deterioration of laser stripe contrast due to cumulative spectral interference and the displacement of essential feature points caused by thermal deformation. Unlike previous methodologies, our approach integrates the joint optimization of channel-wise spectral weighting and spatial centroid alignment, thereby achieving unparalleled sub-pixel precision under dynamic industrial conditions.