这篇论文用迁移学习改造BiSeNetV2，把焊接焊缝分割的Joint IoU从50%多干到81%，零IoU失败恢复率96%，而且不改模型大小和速度，搞建筑焊接的可以看看。
该研究提出一种基于迁移学习和混合Cross-Entropy-Lovász损失的焊缝分割框架，以解决建筑焊接中光照、反射和薄几何形状导致的分割退化问题。在BiSeNetV2骨干网上，新方法达到81.76% Joint IoU和90.73% mIoU，较OHEM基线提升+22.36个百分点，且保持相同FLOPs和推理速度。在反射条件下，该方法恢复了96.33%的严重零IoU失败案例。与DeepLabV3+、UNet、SegFormer的对比验证了该优化策略对轻量实时分割架构的有效性。
Enhanced Seam Segmentation for Automated Welding Robot in Construction Through Transfer Learning: Addressing Limitations of Bilateral Segmentation Network
Reliable seam segmentation is essential for autonomous robotic welding in construction, where harsh illumination, specular reflections, and thin weld geometries often degrade segmentation performance. This study proposes a reflection-robust seam segmentation framework that enhances a BiSeNetV2 backbone through transfer learning and a hybrid Cross-Entropy--Lovász loss. Rather than increasing architectural complexity, the proposed framework improves reflection robustness through learning-stability-oriented optimization. Experimental results show that the proposed method achieves 81.76\% Joint IoU and 90.73\% mIoU, improving Joint IoU by +22.36 percentage points over the OHEM-based baseline while maintaining identical FLOPs, parameter count, and inference speed. The proposed approach also recovers 96.33\% of severe zero-IoU failure cases under reflective conditions. Comparative experiments across BiSeNetV2, DeepLabV3+, UNet, and SegFormer further demonstrate that the proposed optimization strategy is particularly effective for lightweight real-time segmentation architectures. Qualitative analyses additionally show improved seam continuity and reflection robustness in challenging welding environments. These findings suggest that the proposed framework provides a practical and lightweight perception solution for robotic welding applications involving reflective metallic surfaces.