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开发一个软硬件综合体,用于监控生产系统中的过程
Vadim Pechenin1, Rustam Paringer2, Nikolay Ruzanov1
1Research Laboratory 'Artificial Intelligence in Production Systems', Samara National Research University, Moskovskoye shosse 34, 443086 Samara, Russia.
一个新的硬件软件系统使用先进的神经网络自动识别生产部件. 该系统达到93%的准确性,大大改进了传统方法,以更好地计算生产过程.
科学领域:
- 工业自动化 工业自动化
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 目前的生产过程会计方法缺乏效率和自动化.
- 在生产容器中自动识别零件对于生产率至关重要.
- 现有的物体检测方法往往需要大量的训练数据集.
研究的目的:
- 开发一个硬件-软件复杂的增强生产过程会计.
- 为了自动识别生产容器中的零件和补充标记.
- 提高工业环境中对象检测的准确性和效率.
主要方法:
- 开发一个硬件软件综合体,包括一台迷你计算机,摄像头和通信模块.
- 使用卷积神经网络 (YOLO和VGG19) 实现级联算法,用于标签和对象检测.
- 通过级联算法,通过减少样本大小来训练神经网络.
主要成果:
- 开发的系统在细节检测方面表现出93%的准确性.
- 与传统方法相比,级联算法显著提高了识别精度.
- 该系统成功地自动识别了零件和补充标记.
结论:
- 开发的硬件-软件复合体有效地提高了生产过程的会计生产率.
- 与YOLO和VGG19一起的级联算法为对象检测提供了更准确,更有效的数据处理方法.
- 该系统代表了自动化工业部件识别的重大进步.
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