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
概括
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.
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.
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