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New framework enhances welding robot seam segmentation with transfer learning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework to improve seam segmentation for automated welding robots in construction, addressing challenges like harsh lighting and reflections. The approach enhances the BiSeNetV2 model using transfer learning and a hybrid loss function, focusing on learning-stability optimization rather than architectural complexity. This method significantly improves performance, achieving an 81.76% Joint IoU and recovering 96.33% of failure cases under reflective conditions, while maintaining efficiency for real-time applications. AI

IMPACT Improves perception capabilities for robotic welding, potentially increasing automation and precision in construction.

RANK_REASON The cluster contains two identical arXiv papers detailing a new research methodology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework enhances welding robot seam segmentation with transfer learning · 2 sources tracked

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The cluster contains two identical arXiv papers detailing a new research methodology.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Keonvin Park, Yong Ann Voeurn, Hyeokjun Kweon, Doyun Lee ·

    Enhanced Seam Segmentation for Automated Welding Robot in Construction Through Transfer Learning: Addressing Limitations of Bilateral Segmentation Network

    arXiv:2607.06150v1 Announce Type: cross Abstract: 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 reflect…

  2. arXiv cs.LG TIER_1 English(EN) · Doyun Lee ·

    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 enhanc…

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