A novel semantic segmentation network for narrow weld seam recognition based on spatio-temporal feature fusion

Abstract Structured light measurement is widely used in weld seam recognition due to its high precision and robust performance. However, in the automated guided welding process of narrow weld seam, robust detection cannot be achieved using traditional laser vision sensors due to complex environmental interference and the lack of distinct geometric contour information in the weld. To accurately extract narrow weld seam information and enhance image detection stability, we propose a semantic segmentation network for narrow weld seam based on spatio-temporal feature fusion. First, we utilize an enhanced laser vision sensor equipped with an auxiliary ambient light source to autonomously capture high-quality sequence images of narrow weld seam with laser stripe at the engineering site. Second, we design the spatio-temporal feature fusion narrow weld seam network, and a topology encoder is introduced to extract the target’s topological information. Subsequently, a spatial perception module and a temporal feature extraction module are proposed to capture the target’s spatio-temporal information. The feature pyramid fusion module employs multi-level fusion to ultimately output precise weld detection results. Performance on our self-constructed NWSDataset demonstrates that our network effectively addresses real-time detection of narrow weld seam. It has been successfully deployed in submerged arc welding engineering applications, meeting practical welding requirements.