# Improved U-Net++ with Patch Split for Micro-Defect Inspection in Silk Screen Printing
## 1 Introduction  
To strengthen the competitiveness of manufacturing companies in response to diverse consumer demands, many manufacturing companies are turning to multi-product production [1; 2]. In the silk screen printing industry, the types of products can change in a short period of time, and product quality inspection is also evolving from manual inspection by workers or using existing machine vision inspection algorithms to using deep learning-based methods [3; 4; 5]. When visual inspection is carried out by the operator, the type of defect also changes whenever the variety of the material is changed, making it difficult to inspect fine defects, which can lead to market leakage of defective products. Recently, various services have been developed using deep learning technology for numerous computer vision tasks. Deep learning techniques are often applied for automatic quality inspection because quality control in the manufacturing industry involves many repetitive tasks by workers. To this end, MVec has released the MVec anomaly detection (AD) dataset and many studies on vision quality inspection are conducted using this dataset [6]. Silk screen printing is a printing method in which a hole is made using light and ink is then applied through the hole. Quality inspection of silk screen-printed products requires workers with several years of experience in conducting quality inspections. However, even skilled workers cannot avoid eye fatigue and human error, thus, being unable to carry out the work for more than a certain period. Quality standards are judged qualitatively rather than quantitatively [7]. In such a situation, bad market outflow occurs and production has difficulty responding to this, whereby the competitiveness of the company in the marketis inevitably reduced. In such a situation, bad market outflow occurs and production has difficulty responding to this, whereby the competitiveness of the company in the market is inevitably reduced. For these silk-screen printing companies, quality inspection automation, that is, a deep learning-based vision system, is necessary. Silk print defect detection requires very small defects to be detected while inspecting various products at the same time. There are many different types of defects and there are cases in which one must shine light from a specific angle to see it. Therefore, it is necessary to verify the lighting to accurately identify each defect. In addition, in the case of silk prints, the size of the product is diverse and the size of the defect is very small compared to the size; therefore, it is necessary to study this process in detail. In this study, the aim was to determine whether small defects could be detected in one product. A classification method, such as patch distribution modeling (PaDiM), shows high performance in anomaly detection, even when trained on a small dataset [8]. One-class classification, such as using PaDiM, judges abnormality with respect to what is defined as normal, so establishing the normality is important [9; 10]. However, it is difficult to define the normal data acquired for each shooting point. In addition, because of multi-variety characteristics, it is impossible to define normality. Therefore, in this study, segmentation was performed using general supervised learning [11; 12]. As mentioned earlier, the size of silk print defects is very small compared to the product size. To solve this problem, patch sampling was considered without losing the information in the image. Additionally, a learning and inference technique was applied. Patch sampling is a method that is used when the original image cannot be input into the graphics processing unit (GPU) memory [13]. The convolutional neural network (CNN) model has problems such as unnecessary redundancy of overlapping patches, and a tradeoff between localization accuracy and use of context is required [14; 15]. However, U-Net is capable of precise analysis because of its increased upscaling ability inherent in its symmetric architecture with skip-connection [16].