在大学实验室研究和应用安全危险感知和责任可追溯系统
Rundong Liu1, Yuxuan Ding1, Xiujin Zhu1
1School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了用于大学实验室安全的AI系统,增强了危险感知和责任追踪. 它改善了对危险行为的监督,使安全管理更有效和负责任.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 实验室的安全工作.
背景情况:
- 大学实验室安全管理在监督高频风险行为和确保责任可追溯性方面面临挑战.
- 当前的方法经常与"可见但无法控制"的行为作斗争,导致问责问题.
研究的目的:
- 为实验室安全危险感知和责任可追溯性提出基于深度学习的系统.
- 开发一种智能技术解决方案,以加强实验室安全治理.
主要方法:
- 使用了YOLOv5s对象检测模型,集成了SE频道注意力和NWD损失功能.
- 实现了多目标跟踪的DeepSORT,以在实验室环境中实现实时危险感知.
- 开发了一个责任匹配算法和视觉可追溯机制,用于全面的管理系统.
主要成果:
- 拟议的YOLO-lab系统在实验室场景中实现了87.8%的平均平均精度 (mAP).
- 展示了出色的多目标追踪能力,以追踪一个不穿实验服的实验者为例.
- 成功生成责任可追溯性报告,解决传统监督的局限性.
结论:
- 开发的系统有效地解决了传统实验室安全监督的局限性,通过实现可追溯和经过证明的问责制.
- 为提高大学实验室安全治理提供智能和技术途径.
- 提高危险的感知和可追溯性,为更安全的研究环境做出贡献.
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