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终端到终端的泡大小分布检测技术在密集的泡流基于你只看一次的架构
Mengchi Chen1, Cheng Zhang2, Wen Yang1
1College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种深度学习方法,用于精确测量密集泡泡流中的泡泡大小分布. 这种新技术提高了精度和回忆力,为化学生产分析提供了重大进步.
科学领域:
- 流体动力学 流体动力学
- 化学工程是化学工程的重要组成部分.
- 计算机视觉 计算机视觉
背景情况:
- 精确的气泡大小分布 (BSD) 测量对于了解气液质量转移和化学生产至关重要.
- 由于算法限制和数据复杂性,现有的方法与高密度的泡流作斗争.
研究的目的:
- 开发一个端到端的深度学习方法,用于在密集的泡流中精确检测BSD.
- 为了克服当前测量技术中有限的图像算法和高数据密度所带来的挑战.
主要方法:
- 利用深度学习,特别是你只看一次 (YOLO) 版本7,用于对象检测气泡.
- 实现了同时圆参数适配用于泡大小测量.
- 修改了完整的交叉与结合 (CIoU) 计算,并优化了 L2 约束的损失函数,以提高准确性.
主要成果:
- 实现了高性能指标:精度为0.9871,回忆率为0.8725,平均平方误差为3.8299.
- 与现有的BSD测量方法相比,在密集的泡流中证明了更高的准确性和效率.
结论:
- 拟议的深度学习技术为在具有挑战性的高密度泡流条件下测量BSD提供了高效和准确的解决方案.
- 该方法显示了化学工程和相关领域实际应用的巨大潜力.
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