Uncertainty-Aware Laser Stripe Segmentation With Nonlocal Mechanisms for Welding Robots

Abstract

The line-structured-light system has been widely adopted for weld seam reconstruction and tracking in intelligent welding robots. However, extracting projected laser stripes from captured images remains a significant challenge due to the presence of intense noises in welding environments. In this work, we propose an a uncertainty-aware nonlocal laser stripe segmentation network (UNLS-Net) to achieve precise laser stripe extraction under real-world, challenging welding conditions. The proposed framework designs an uncertainty-aware policy that refines coarse segmentation results using combined epistemic-aleatoric uncertainty maps. In addition, nonlocal attention modules are incorporated to enhance spatial correlation, thereby preserving the continuity of laser stripes. Comprehensive experiments are conducted on our large-scale, shape-diverse laser stripe dataset comprising 3136 welding images with varying weld seam geometries, sizes, and noise profiles. The proposed method demonstrates superior performance compared to existing segmentation approaches, achieving significant improvements in both laser stripe continuity and denoising effectiveness.