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.

翻译：紫外线（UV）杀菌辐射是医疗环境中一种成熟的非接触式表面消毒方法。传统方法需要大量人工干预来定义消毒区域，使自动化变得复杂；而基于深度学习的方法通常需要大量微调和大型数据集，这在大规模部署中可能不切实际。此外，这些方法通常未解决部分表面消毒的场景理解问题，而这对于避免意外的紫外线暴露至关重要。我们提出了一种解决方案，利用基础模型简化基于机械臂的紫外线消毒表面选择，减少人工参与并无需模型训练。此外，我们提出了一种视觉语言模型辅助的分割细化方法，用于检测并排除薄而小的非目标物体，结果表明这减少了误分割错误。我们的方法在正确分割目标和非目标表面方面实现了超过92%的成功率，通过机械臂和模拟紫外线的真实世界实验证明了其在实际应用中的潜力。