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 reflection-robust seam segmentation framework that enhances a BiSeNetV2 backbone through transfer learning and a hybrid Cross-Entropy--Lov\'asz loss. Rather than increasing architectural complexity, the proposed framework improves reflection robustness through learning-stability-oriented optimization. Experimental results sho
The increasing sophistication of AI and computer vision models, coupled with robust robotic platforms, is enabling significant advancements in automated construction processes at this moment.
Improved autonomous welding capabilities are crucial for enhancing efficiency, safety, and precision in construction, addressing labor shortages, and boosting productivity.
Welding operations in harsh industrial environments can become more reliable and less reliant on manual intervention, accelerating automation in construction and manufacturing.
- · Construction companies
- · Robotics manufacturers
- · Automation software developers
- · Industrial AI solution providers
- · Traditional manual welding services
- · Companies slow to adopt automation
Automated welding robots become more pervasive in construction and other heavy industries.
Reduced labor costs and improved safety lead to increased investment in large-scale infrastructure projects.
The broader adoption of AI-driven robotics accelerates the development of more complex autonomous construction systems, potentially leading to entirely automated job sites.
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Read at arXiv cs.LG