# Fv-AD: F-AnoGAN Based Anomaly Detection in Chromatic Process for Smart Manufacturing
## 1 Introduction  
Supervised learning is difficult to predict and is limited to areas where abnormal data are collected. Recently, unsupervised learning or semi-supervised learning models have been increasingly used in anomaly detection. The GAN, an unsupervised learning model, has features that can solve data imbalance problems and is widely used for anomaly detection in abnormal areas [1, 2]. Furthermore, we have successfully demonstrated learning and deep generative models. The proposed GAN-based anomaly detection method detects anomalies using only the normal data generated by learning. The GAN-based anomaly-detection model is being developed, and the GAN model is actively being studied in the image field. Recently, semi-supervised learning has been used to improve classification problems [3]. Anomaly detection is a method to distinguish between normal and abnormal (bad) data, and making a distinction between the two is a critical issue in all domains. AI can preferentially classify products that are expected to be defective during the manufacturing process, resulting in. Furthermore, AI has the advantage of first reviewing and shorter working hours suggesting products that are deemed defective, such as a lack of expertise ofworkers and human error. Most anomaly detection predictions are available in a variety of domains but require an annotation process for unusual data. Furthermore, data collection in the manufacturing process industry is difficult because the frequency of defective samples is significantly lower than that of normal samples [4]. Therefore, domains with severe data imbalances require access to unsupervised learning-based anomaly detection. Therefore, in this paper, we learn and generate only normal data using GAN, then input the data into the learned GAN model, compare the original image with the original image, and predict it [5]. We also propose a model that augments the discriminator portion of the model with a visualization portion of the output to make the outlier position more intuitive and descriptive. A data imbalance impedes anomaly detection in the manufacturing process. Various studies are being conducted to address these issues using batch sampling [6] and augmentation techniques. In addition, we expect to solve this problem if we proceed with learning only through normal data as one of the various studies [7]. Therefore, this study applies fast anomaly detection with the GAN (F-AnoGAN) technique to manufacturing process data that are difficult to label. In the absence of abnormal data in the manufacturing process, the GAN technique can only be used only with normal data by generating and learning arbitrary data. AnoGAN is the first study to utilize GAN for anomaly detection and uses the F-AnoGAN technique, a method developed in AnoGAN, for manufacturing process data. Localization also highlights damaged areas of an image, making them visible [8]. Among the GAN models, we propose the detection and localization of anomalies in the chromate process using the F-AnoGAN technique, which is an anomaly detection GAN. The anomalous part of the image is represented as an image and its performance is verified and demonstrated by the loss value and anomaly score. Furthermore, the F-AnoGAN model proposed in this paper is intended for real-world anomaly detection and utilization, and it outperforms previous Anomaly GAN(AnoGAN) models in quantitative indicators [9]. This paper proposes a prediction method of outlier detection in the quality check process through image data generated by the chromate process. The predictive performance of the model through the proposed F-AnoGAN and the visualization through it proved to be significant. Suggest that image data from the manufacturing process can contribute to one of the prediction methods of outlier detection. The rest of this paper is structured as follows Section 2 introduces the anomaly detection cases and existing anomaly detection models used in the smart factory.