在建筑工业中新兴的不安全行为识别的无排练的持续学习
Tao Wang1, Saisai Ye2, Zimeng Zhai2
1Department of Campus Security, Ocean University of China, Qingdao 266000, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一个AI模型,用于识别建筑中的不安全行为,适应新的风险而不忘记旧的风险. 它通过改善工人和现场监控来提高工业5.0的安全性.
科学领域:
- 人工智能的人工智能
- 工业安全 工业安全 工业安全
- 机器学习 机器学习
背景情况:
- 工业5.0强调以人为中心,可持续和有弹性的工业实践,但建筑仍然是人工密集型的,手动安全判断.
- 当前的人工智能行为识别模型很难适应新的不安全行为,而不会失去对现有的知识.
- 深层卷积神经网络虽然有效,但通常受到其预定义的多路分类结构的限制.
研究的目的:
- 开发一种多功能和高效的AI识别模型,用于识别建筑环境中的不安全行为.
- 为了使模型能够不断学习新的不安全行为,同时保留先前学习的类别的知识.
- 解决传统分类器在适应动态工业环境中新出现的安全风险方面的局限性.
主要方法:
- 提出了一种持续学习方法,将任务不变和任务特定提示集成到预先训练的模型中.
- 注射提示到结的脊柱的特定层,以指导学习,没有排练缓冲区.
- 引入了Split-UBR数据集,这是建筑业持续不安全行为识别的基准.
主要成果:
- 拟议的模型在准确性方面表现出卓越的性能,并在Split-UBR数据集上减少遗忘.
- 对比实验验证实了该模型的有效性与最先进的持续学习基线相比.
- 该方法成功地识别了建筑场景中的新和以前学习的不安全行为.
结论:
- 开发的AI模型为建筑行业的动态不安全行为识别提供了有效的解决方案.
- 具有互补提示的持续学习方法提高了AI安全系统中的适应性和知识保留能力.
- 这项研究在工业5.0框架内为更安全,更有弹性的工业环境做出了贡献.
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