Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target items that are to be classified are generally excluded from existing databases, and the manual collection of images poses certain limitations. Therefore, to overcome the data deficiency that is present in many domains including manufacturing, we propose a method of generating new training images via image pre-processing steps, background elimination, target extraction while maintaining the ratio of the object size in the original image, color perturbation considering the predefined similarity between the original and generated images, geometric transformations, and transfer learning. Specifically, to demonstrate color perturbation and geometric transformations, we compare and analyze the experiments of each color space and each geometric transformation. The experimental results show that the proposed method can effectively augment the original data, correctly classify similar items, and improve the image classification accuracy. In addition, it also demonstrates that the effective data augmentation method is crucial when the amount of training data is small.

中文翻译：

---

基于逆学习信噪比的颜色扰动和几何变换的数据增强方法基于深度学习的目标识别

深度学习被应用于各种制造领域。要训​​练深度学习网络，我们必须收集足够数量的训练数据。但是，很难收集训练网络以执行对象识别所需的图像数据集，尤其是因为通常将要分类的目标项从现有数据库中排除，并且手动收集图像会带来某些限制。因此，为了克服包括制造在内的许多领域中存在的数据不足，我们提出了一种通过图像预处理步骤，背景消除，目标提取生成新训练图像的方法，同时保持原始图像中对象尺寸的比例，考虑原始图像和生成图像之间的预定义相似性，几何变换，和转移学习。具体来说，为了演示颜色扰动和几何变换，我们比较并分析了每种颜色空间和每种几何变换的实验。实验结果表明，该方法可以有效地增加原始数据，对相似项进行正确分类，提高图像分类的准确性。此外，它还证明了有效的数据扩充方法在训练数据量较小时至关重要。并提高图像分类的准确性。此外，它还证明了有效的数据扩充方法在训练数据量较小时至关重要。并提高图像分类的准确性。此外，它还证明了有效的数据扩充方法在训练数据量较小时至关重要。