Enhanced seam segmentation for automated welding robot in construction through transfer learning: addressing...

Reliable weld seam segmentation is essential for autonomous robotic welding in construction, where severe illumination changes, specular reflections, and thin weld geometries frequently degrade segmentation performance. This paper proposes a lightweight post-training optimization framework that systematically improves existing real-time semantic segmentation networks without modifying their architectures. Starting from an Online Hard Example Mining (OHEM)-pretrained checkpoint, the proposed framework combines controlled fine-tuning with a hybrid Cross-Entropy-Lovász objective to enhance pixel-level classification, region-level seam continuity, and recovery from reflection-induced segmentation failures while preserving real-time inference efficiency. Extensive experiments demonstrate that the proposed framework substantially improves segmentation performance. Using BiSeNetV2, Joint IoU increases from 59.40% to 81.76% (+22.36 percentage points), while mIoU reaches 90.73% without increasing parameter count, FLOPs, inference latency, or memory consumption. Furthermore, the proposed framework successfully recovers 96.33% of severe zero-IoU failure cases caused by strong reflections, substantially improving the operational reliability of downstream robotic perception. To evaluate the generality of the proposed optimization framework, additional experiments were conducted using U-Net, DeepLabV3+, SegFormer-B0, and PIDNet-S under multiple fine-tuning configurations. The results reveal that the effectiveness of post-training optimization is strongly architecture-dependent: lightweight real-time segmentation networks, particularly BiSeNetV2 and PIDNet-S, consistently benefit from the proposed framework, whereas larger semantic-oriented architectures exhibit comparatively smaller and less stable improvements. In robotic welding experiments, the proposed BiSeNetV2 model was the only evaluated approach capable of consistently generating a valid weld seam trajectory, achieving an average joint-center offset of [Formula: see text] mm during robotic path planning, corresponding to an approximately 9.8 × reduction compared with the previous BiSeNetV2-based robotic welding system. Overall, the results demonstrate that carefully designed post-training optimization provides a practical and computationally efficient alternative to architectural redesign, substantially improving failure recovery, seam continuity, and robotic trajectory generation for autonomous welding in reflective construction environments.