用视觉算法让同步辐射光束线自动避障，减少人工干预。
Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision
- 通过多视角摄像头和深度学习实现设备实时分割与跟踪。
- 在真实光束线上达到高精度碰撞检测，响应速度满足实时要求。
- 支持新物体类别快速适应，适合科研人员与自动化系统集成。
同步辐射光源作为前沿大型用户设施，亟需实现全自动光束线操作，以在最小人工干预下安全、可靠地开展实验。然而现有先进光束线仍严重依赖人工安全监督。为弥合自动化与自主化之间的差距，本文提出一种基于计算机视觉的系统，结合深度学习与多视角相机实现实时碰撞检测。该系统利用设备分割、跟踪与几何分析，并通过迁移学习提升鲁棒性。同时开发了交互式标注模块，增强对新物体类别的适应能力。在真实光束线数据集上的实验表明，系统具备高精度、实时性能，展现出显著的自主运行潜力。
原文摘要 · Abstract (English)
The synchrotron light source, a cutting-edge large-scale user facility, requires autonomous synchrotron beamline operations, a crucial technique that should enable experiments to be conducted automatically, reliably, and safely with minimum human intervention. However, current state-of-the-art synchrotron beamlines still heavily rely on human safety oversight. To bridge the gap between automated and autonomous operation, a computer vision-based system is proposed, integrating deep learning and multiview cameras for real-time collision detection. The system utilizes equipment segmentation, tracking, and geometric analysis to assess potential collisions with transfer learning that enhances robustness. In addition, an interactive annotation module has been developed to improve the adaptability to new object classes. Experiments on a real beamline dataset demonstrate high accuracy, real-time performance, and strong potential for autonomous synchrotron beamline operations.
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