A Deep Learning-Based Framework for Offline Robotic Weld Path Generation Using a Single Top-View RGB-D Image

Pipe welding automation requires accurate weld seam extraction and reliable robotic weld path generation under complex geometric conditions. Existing vision-based approaches often rely on expensive laser sensing systems, multi-view sensing, or continuous seam tracking, resulting in increased hardware cost and system complexity. To address these limitations, this study proposes a deep learning-based offline robotic welding framework that generates a three-dimensional welding path from a single top-view Red–Green–Blue and Depth (RGB-D) image acquired prior to welding. The proposed framework integrates weld seam detection, semantic segmentation, morphology-based post-processing, RGB-D image alignment, coordinate transformation, and polynomial-based trajectory refinement into a unified pipeline for robotic weld path generation. A custom pipe welding dataset consisting of 1476 annotated images collected from representative industrial pipe materials with varying diameters was constructed to evaluate the proposed framework. The experimental results demonstrate that the proposed Region of Interest (ROI)-guided weld seam extraction pipeline improves the U-Net segmentation performance from 0.735 to 0.791 mean Intersection over Union (mIoU), while the detection model achieves a Recall of 0.988 and an mean Average Precision at an Intersection over Union threshold of 0.5 (mAP50) of 0.995. Furthermore, polynomial-based trajectory refinement reduces the three-dimensional positional root mean square error (RMSE) to 0.333 mm, enabling continuous robotic welding over the entire visible weld seam without additional path modification. These results demonstrate that the proposed framework provides a practical and cost-effective solution for offline robotic weld seam extraction and weld path generation, while establishing a promising foundation for future extension toward online robotic welding through real-time weld seam tracking and adaptive trajectory correction.