T. I. Erdei, Tibor Péter Kapusi, András Hajdu, G. Husi
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引用次数: 0
摘要
工业 4.0 已成为当今工业科学最主要的研究领域之一。许多工业机械装置在调试过程中并不具备使用图像分析技术的现代标准。我们现有的机械设备无法实现智能材料处理、分拣和物体识别。因此,我们为现有的机器人设备提出了一种新颖的深度学习方法,无需修改即可应用于未来的机器人。在实施过程中,我们还为植入机器设计了需要识别的 PCB 继电器模块的 3D CAD 模型。此外,我们还利用 FDM 3D 打印技术开发并制造了用于组装铝型材的部件,专门用于分拣目的。我们还在三维 CAD 模型的基础上应用深度学习算法,利用 CGI 渲染技术生成用于分类的对象数据集。我们生成了两个数据集,并应用图像到图像的转换技术来训练深度学习算法。合成后的图像具有足够的信息含量和质量,可以有效地训练深度学习算法。因此,我们提出了一种数据集翻译方法,适用于重新生成原始数据集可能具有挑战性的情况。我们对获得的数据集结果进行了分析和评估。
Image-to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems
Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset.