## 1 Introduction  
Recent advances in the Internet of Things, big data, cloud computing, and Industry 4.0 are rapidly revolutionizing the manufacturing industry. Industry 4.0, which is related to automation and digitization, is closely linked to smart factories. Smart factories describe the intelligent factory of the future and their core technologies are artificial intelligence (AI) and robot vision systems [1, 2, 3, 4]. Owing to the development of AI-related technologies, such as deep learning algorithms, the computational power associated with the growth of graphics processing units, and the collection of big data using large-scale sensor networks, AI has been extensively researched. Furthermore, it has undergone rapid development. Robot vision is the prime perception channel used in manufacturing-related technologies [5]. Accordingly, vision systems have been used in a diverse range of applications in the manufacturing field such as inspection monitoring systems, manipulation, picking and placing, object recognition, and mobile robotics [6, 7, 8, 9]. Specifically, combining deep learning, vision systems, and smart factories is a recent trend in the manufacturing sector. Deep learning has been utilized in various manufacturing applications, including autosorting systems, inspection systems, maintenance in mechanical manufacturing, fault classification and diagnosis, and classification systems. For example, to pick and place objects using a manipulator, the objects must be recognized and classified using deep learning and a robot vision system. Object recognition and classification can be achieved using deep learning [10, 11, 12, 13, 14]. However, there is a crucial problem in applying deep learning. For example, a network trained to classify objects into categories may be considered. In addition, some noise may be added to the image that falls under a specific category in the trained network. In this case, the noisy image may be misclassified, although the original image without noise is classified correctly. In other words, noise adversely affects the accuracy of image classification. Thus, deep learning models are vulnerable to noise attacks. An autonomous driving system applying deep learning can get into an accident if it is attacked with a noisy image. Such attacks are called adversarial attacks [15; 16]. Another problem when applying deep learning algorithms is that it is necessary to prepare sufficient training images to train the deep learning network. Numerous diverse datasets that can be used as training images are available on the Internet. However, in some cases, it is difficult to collect the image data of unusual objects, which are not included in the training dataset of the deep learning model, as opposed to common items. Specifically, in the manufacturing field--for instance, in the electronics industry--many objects are uncommon. There is a limit to capturing and collecting the image data of unusual objects. Therefore, a data augmentation method that can generate images automatically is required to overcome data deficiency. Many researchers are studying data augmentation methods toward this end. Data augmentation improves the generalization capabilities of the deep learning network and the performance of the classification model [17; 18]. In this paper, a data augmentation method is proposed to achieve noise robustness and overcome data deficiencies. Our study makes three main contributions to the literature as follows:  
* Automatic determination of the optimal perturbation range based on image similarity;
* Weight calculation of color perturbation based on the characteristics of the color distribution of training images;
* Data augmentation based on color perturbation and geometric transformations to compensate data deficiency and noisy images. The proposed method suggests augmenting training data based on the concept of color jittering.