From Detection to Action: Implementing Deep Learning Inference in PLC Systems via Docker Services

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2024-01-01 DOI:10.1016/j.ifacol.2024.07.365

Körösi L., Kajan S., Berki M., Skirkanič J., Lúčny M., Melichar A., Mihálik J.

引用次数: 0

Abstract

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment.

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从检测到行动：通过 Docker 服务在 PLC 系统中实施深度学习推理

本文重点介绍基于人工智能（AI）的可编程逻辑控制器（PLC）识别和检测算法的实现。基于深度神经网络（DNN）的人工智能方法具有大量隐藏层和隐藏层中神经元的特点，这增加了计算人工神经网络（ANN）输出的计算复杂性。目前流行的卷积神经网络（CNN）包括 ResNet、AlexNet、GoogLeNet 等。本文提出了一种计算 CNN 输出的远程云解决方案。在物体识别和检测应用中对 CNN 的实现进行了测试。CNN 和 YOLO 检测器的训练和测试在 Python 环境中进行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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