用大模型自动选消毒表面，省去人工和训练
Autonomous Surface Selection For Manipulator-Based UV Disinfection In Hospitals Using Foundation Models
- 用基础模型自动识别需消毒的表面，无需额外训练
- 92%以上准确率分割目标与非目标区域
- 适合医院机器人消毒场景，减少误照风险
紫外线杀菌是医疗环境中的成熟非接触式表面消毒方法。传统方法需大量人工划定消毒区域，难以自动化；基于深度学习的方法常需大量微调和数据集，不适用于大规模部署。此外，现有方法常忽略部分表面消毒的场景理解，易造成意外紫外线暴露。本文提出一种利用基础模型实现机械臂紫外消毒表面自动选择的新方案，大幅减少人工干预，且无需模型训练。同时引入视觉-语言模型辅助分割优化，可有效检测并排除细小非目标物体，降低误分割误差。实验显示，该方法在目标与非目标表面分割上成功率超过92%，真实机械臂与模拟紫外线实验验证了其实际应用潜力。
原文摘要 · Abstract (English)
Ultraviolet (UV) germicidal radiation is an established non-contact method for surface disinfection in medical environments. Traditional approaches require substantial human intervention to define disinfection areas, complicating automation, while deep learning-based methods often need extensive fine-tuning and large datasets, which can be impractical for large-scale deployment. Additionally, these methods often do not address scene understanding for partial surface disinfection, which is crucial for avoiding unintended UV exposure. We propose a solution that leverages foundation models to simplify surface selection for manipulator-based UV disinfection, reducing human involvement and removing the need for model training. Additionally, we propose a VLM-assisted segmentation refinement to detect and exclude thin and small non-target objects, showing that this reduces mis-segmentation errors. Our approach achieves over 92\% success rate in correctly segmenting target and non-target surfaces, and real-world experiments with a manipulator and simulated UV light demonstrate its practical potential for real-world applications.
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