摘 要
工业自动化生产线质量检测是现代工业生产中至关重要的环节，而机器视觉技术在提高质量检测准确性和效率方面发挥着重要作用。本文针对工业自动化生产线质量系统的设计与数据采集，以及深度学习模型的训练与优化进行了研究。在深度学习模型的训练与优化方面，我们首先介绍了数据集的构建与标注方法，包括数据样本的收集和标签的标注过程。接着，我们讨论了深度学习模型的选择和设计，包括卷积神经网络和循环神经网络等模型的应用。最后，我们探讨了训练过程中的参数调优和优化策略，以提高模型的准确性和鲁棒性。
关键词：工业自动化；生产线质量；机器视觉
Abstract
The quality inspection of industrial automation production line is a vital part of modern industrial production, and machine vision technology plays an important role in improving the accuracy and efficiency of quality inspection. In this paper, the design and data collection of the quality system of the industrial automation production line, as well as the training and optimization of the deep learning model, are studied. In terms of training and optimization of deep learning models, we first introduce the construction and labeling methods of datasets, including the collection of data samples and the labeling process. Next, we discussed the selection and design of deep learning models, including the application of models such as convolutional neural networks and recurrent neural networks. Finally, we discuss the parameter tuning and optimization strategies in the training process to improve the accuracy and robustness of the model.
Keywords: Industrial automation; production line quality; machine vision
目 录
1 引言 1
2 相关理论介绍 1
2.1 工业自动化生产线质量检测的现状 1
2.2 机器视觉在工业自动化中的应用概述 2
3 工业自动化生产线质量系统设计与数据采集 2
3.1 生产线质量检测系统的设计流程 2
3.2 机器视觉技术在质量检测中的应用 3
3.3 数据采集与预处理方法 3
4 工业自动化生产线质量系统深度学习模型的训练与优化 4
4.1 数据集构建与标注方法 4
4.2 深度学习模型的选择和设计 5
4.3 训练过程与优化策略 6
5 结论 6
致 谢 8
参考文献 9
工业自动化生产线质量检测是现代工业生产中至关重要的环节，而机器视觉技术在提高质量检测准确性和效率方面发挥着重要作用。本文针对工业自动化生产线质量系统的设计与数据采集，以及深度学习模型的训练与优化进行了研究。在深度学习模型的训练与优化方面，我们首先介绍了数据集的构建与标注方法，包括数据样本的收集和标签的标注过程。接着，我们讨论了深度学习模型的选择和设计，包括卷积神经网络和循环神经网络等模型的应用。最后，我们探讨了训练过程中的参数调优和优化策略，以提高模型的准确性和鲁棒性。
关键词：工业自动化；生产线质量；机器视觉
Abstract
The quality inspection of industrial automation production line is a vital part of modern industrial production, and machine vision technology plays an important role in improving the accuracy and efficiency of quality inspection. In this paper, the design and data collection of the quality system of the industrial automation production line, as well as the training and optimization of the deep learning model, are studied. In terms of training and optimization of deep learning models, we first introduce the construction and labeling methods of datasets, including the collection of data samples and the labeling process. Next, we discussed the selection and design of deep learning models, including the application of models such as convolutional neural networks and recurrent neural networks. Finally, we discuss the parameter tuning and optimization strategies in the training process to improve the accuracy and robustness of the model.
Keywords: Industrial automation; production line quality; machine vision
目 录
1 引言 1
2 相关理论介绍 1
2.1 工业自动化生产线质量检测的现状 1
2.2 机器视觉在工业自动化中的应用概述 2
3 工业自动化生产线质量系统设计与数据采集 2
3.1 生产线质量检测系统的设计流程 2
3.2 机器视觉技术在质量检测中的应用 3
3.3 数据采集与预处理方法 3
4 工业自动化生产线质量系统深度学习模型的训练与优化 4
4.1 数据集构建与标注方法 4
4.2 深度学习模型的选择和设计 5
4.3 训练过程与优化策略 6
5 结论 6
致 谢 8
参考文献 9
