发表机构
Interdisciplinary Program in Artificial Intelligence, Seoul National University; Department of Civil Engineering and Construction, Georgia Southern University; The Graduate School of Advanced Imaging Science, Multimedia & Film, Chung-Ang University（首尔国立大学人工智能跨学科项目; 佐治亚南方大学土木工程与建筑系; 中央大学先进影像科学、多媒体与电影研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对建筑自动化焊接中焊缝分割难题，提出通过迁移学习和混合损失增强BiSeNetV2的框架，提升反射鲁棒性。实验表明其联合交并比等指标优异，能恢复多数失败案例，对轻量级实时分割架构有效，为机器人焊接提供实用感知方案。
AI 中文摘要
可靠的焊缝分割对于建筑中的自主机器人焊接至关重要，恶劣照明、镜面反射和薄焊缝几何形状常使分割性能下降。本研究提出一个抗反射的焊缝分割框架，通过迁移学习和混合交叉熵 - 洛瓦斯损失增强BiSeNetV2主干。该框架通过面向学习稳定性的优化提高反射鲁棒性，而非增加架构复杂性。实验结果表明，该方法实现了81.76%的联合交并比和90.73%的平均交并比，在保持相同计算量、参数数量和推理速度的情况下，比基于OHEM的基线提高了22.36个百分点的联合交并比。该方法还在反射条件下恢复了96.33%的严重零交并比失败案例。跨BiSeNetV2、DeepLabV3+、UNet和SegFormer的对比实验进一步证明，该优化策略对轻量级实时分割架构特别有效。定性分析还显示在具有挑战性的焊接环境中，焊缝连续性和反射鲁棒性得到改善。这些发现表明，该框架为涉及反射金属表面的机器人焊接应用提供了实用且轻量级的感知解决方案。
英文摘要
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.
