迈向自主实验室安全监控：通过场景结构学习识别危险
Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure
AI总结：
本文提出通过场景图引导对齐方法，利用视觉语言模型提升实验室安全监控中危险识别的准确性。
摘要：
实验室中轻微的不安全行为可能导致严重伤害，但除了强制性的实验室安全培训外，持续的安全监控受限于人力可用性。视觉语言模型（VLMs）为自主实验室安全监控提供了希望，但其在现实环境中的有效性尚不明确，因为缺乏视觉评估数据，大多数安全事件主要以非结构化文本记录。为解决这一差距，我们首先引入了一个结构化数据生成管道，将文本实验室场景转换为对齐的（图像、场景图、地面真实值）三元组，使用大型语言模型作为场景图建筑师，图像生成模型作为渲染器。我们在包含1,207个样本、362种独特场景和七种开源和闭源模型的合成数据集上的实验表明，VLMs在文本场景图的情况下表现良好，但在仅视觉设置中显著下降，表明难以直接从像素中提取结构化对象关系。为克服这一问题，我们提出了一种后训练上下文工程方法，即场景图引导对齐，通过将视觉输入转换为更符合VLM推理的结构化场景图，弥合VLM中的感知差距，提高仅视觉设置下的危险检测性能。
英文摘要：
Laboratories are prone to severe injuries from minor unsafe actions, yet continuous safety monitoring -- beyond mandatory pre-lab safety training -- is limited by human availability. Vision language models (VLMs) offer promise for autonomous laboratory safety monitoring, but their effectiveness in realistic settings is unclear due to the lack of visual evaluation data, as most safety incidents are documented primarily as unstructured text. To address this gap, we first introduce a structured data generation pipeline that converts textual laboratory scenarios into aligned triples of (image, scene graph, ground truth), using large language models as scene graph architects and image generation models as renderers. Our experiments on the synthetic dataset of 1,207 samples across 362 unique scenarios and seven open- and closed-source models show that VLMs perform effectively given textual scene graph, but degrade substantially in visual-only settings indicating difficulty in extracting structured object relationships directly from pixels. To overcome this, we propose a post-training context-engineering approach, scene-graph-guided alignment, to bridge perceptual gaps in VLMs by translating visual inputs into structured scene graphs better aligned with VLM reasoning, improving hazard detection performance in visual only settings.
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