使用基于视觉的模型,人工智能引导实时检测药物瓶装填充线路的流量不规则和瓶
Sai Vijay Thattukolla1, Sai Vinay Thattukolla2
1CSL Behring, Chicago, Illinois, USA.
Pharmaceutical development and technology
|December 10, 2025
概括
这项研究引入了一种人工智能驱动的计算机视觉系统,可以实时检测药物瓶流量问题,显著提高生产线效率和产品完整性.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 药品填充线需要持续的瓶子流量来保持无菌性并防止中断.
- 不规则的瓶子移动 (空隙,聚类,翻转) 会导致延迟,破碎和延长批次时间.
- 目前的系统缺乏实时检测和提醒小瓶流动不规则.
研究的目的:
- 开发一个人工智能引导的框架,用于实时检测瓶体流动不规则.
- 为了分类药品填充线路上的瓶风险.
- 为提高运营效率提供可操作的警报.
主要方法:
- 使用计算机视觉 (YOLOv8,OpenCV) 开发了一个轻量级的,人工智能引导的框架.
- 高速摄像机捕捉了输送机的镜头,并处理了瓶子位置和流量指标.
- 基于规则的系统对瓶风险进行了分类 (低,中,高).
主要成果:
- 该分类器在操作框架上获得了93.3%的准确性和95.21%的F1-宏分数.
- 该系统以18 FPS (CPU) 和25 FPS (Jetson Nano) 的速度处理.
- 报告了高探测器性能指标 (mAP,精度,回忆,F1).
结论:
- 人工智能解决方案是可扩展的,非侵入性的,可以整合到现有的制药产品线中.
- 基于规则的方法确保了GMP监管环境的可解释性.
- 风险分类器显示高精度 (95.24%),回忆 (95.83%) 和F1 (95.21%).
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