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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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In some cases, there...
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Ophthalmic Drug Delivery Systems

Ophthalmic drug delivery faces major limitations due to poor absorption across the corneal membrane. This process is primarily driven by diffusion and is influenced by two main factors: the physicochemical properties of the drug and tear drainage. Most ophthalmic drugs, such as pilocarpine, epinephrine, atropine, and local anesthetics, are weak bases. They are typically formulated at an acidic pH to enhance chemical stability. However, this leads to high ionization, reducing their ability to...

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相关实验视频

Updated: Jun 23, 2026

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
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实时视觉智能用于药品包装缺陷检测.

Ajantha Vijayakumar1, Subramaniyaswamy Vairavasundaram2, Joseph Abraham Sundar Koilraj1

  • 1School of Computing, SASTRA Deemed University, Thanjavur, 613401, India.

Scientific reports
|August 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了CBS-YOLOv8,这是用于药品缺陷检测的增强YOLOv8模型. 它显著提高了识别平板电脑缺陷的准确性和速度,优化了制造质量保证.

关键词:
计算机视觉 计算机视觉 计算机视觉协调注意力 协调注意力缺陷检测 检测缺陷检测 检测缺陷检测对象检测检测对象检测对象检测这就是YOLOv8的意义.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业自动化 工业自动化

背景情况:

  • 在泡包装中检测药品缺陷是具有挑战性的,传统方法是低效和昂贵的.
  • 使用YOLO模型的自动缺陷检测很普遍,但需要对实时应用程序进行增强.
  • 现有的方法在连续制造环境中与精度和计算开销作斗争.

研究的目的:

  • 提出一个增强的YOLOv8模型,CBS-YOLOv8,用于改善制药制造业的实时缺陷检测.
  • 增强特征提取并减少计算复杂性,以更快,更准确地识别缺陷.
  • 用定制和公共数据集对现有方法验证模型的性能.

主要方法:

  • 该研究通过整合协调注意力来增强YOLOv8架构,以改进功能提取.
  • 加入加权双向特征金字塔网络 (BiFPN),以实现更好的特征融合和减少信息丢失.
  • 实现了一个简单的空间金字塔快速聚合 (SimSPPF) 模块,以减少计算需求并提高速度.
  • 针对有缺陷的药物片的定制数据集被用于模型培训和评估.

主要成果:

  • 在定制数据集上,CBS-YOLOv8实现了97.4%的平均平均精度 (mAP) 和79.25 FPS的推断速度.
  • 该模型在缺陷检测准确度和处理速度方面表现优于其他比较型号.
  • 在SESOVERA-ST盐水瓶填充水位监测数据集中,CBS-YOLOv8获得了99.3%的mAP50.

结论:

  • 在自动化药品缺陷检测方面,CBS-YOLOv8提供了显著的进步.
  • 该模型提供了优化的检查过程,使得能够快速识别和纠正缺陷.
  • 这项技术通过提高准确性和效率来加强制造环境中的质量保证实践.