# Fv-AD: F-AnoGAN Based Anomaly Detection in Chromatic Process for Smart Manufacturing
## 4 Experiment and Results
### Results and Analysis
Compared to AnoGAN, it showed a high resolution, and the last image compares the real image learned through the conventional encoder with the fake image generated by the generator. Compared to Figure 11, we can see the darkened part at the bottom, and through this, we can observe that the crack at the top and the crack at the bottom of Figures 10 and 11 were detected in contrast. Figure 12 shows the result of the abnormal detection of a real image without a crack. The image on the left shows the image data of the normal product obtained during the chromate process. The middle image is an image generated by the generator, and the normal image does not reach the same resolution as the real image but produces a compliant false image. The final image compares the real image learned through the encoder with the fake image generated by the generator. Compared to Figures 11 and 12, the abnormal location of the dark black area was not found, demonstrating that the normal image was correctly detected. Figure 11: Top Crack Image Data Comparison. Figure 10: Bottom Crack Image Data Comparison. Localization is used to detect anomalies, and the location of the anomaly can be checked after learning with F-AnoGAN. As the image was binarized using the threshold technique, the position of the abnormal part is converted to a dark black color. Figure 13 learns chromate process data from the proposed F-AnoGAN and then demonstrates that abnormalities can be judged and predicted using localization and anomaly scores. Table 3 compares the approaches according to encoder training. The same WGAN training was implemented using an unrestricted encoder of a linear output layer. Three versions of the performance are presented and the \(izi_{f}\) encoder model proposed in this paper describes the best performance. We compare \(ziz\), \(izi\), and \(izi_{f}\) encoder training architectures based on WGAN. Although there are various evaluation indicators for measuring the model, in this study, the performance of the model was confirmed and verified using five items as shown in Table 4. The refined data according to the preprocessing of the data has a large impact on accuracy, which can vary depending on the quality of the data, and image data collected in the chromate process boat high performance in all models. The proposed Fv-AnoGAN model exhibited the highest performance. The fast localization anomaly detection (Fv-AD) model was newly defined in this study, and positive and useful results were obtained using data generated in the chromate process in the outlier detection process. The abnormal location of the defective image in this study was determined using unsupervised learning-based F-AnoGAN, which was intended to be tested, and it was proven using outlier scores. Because the post-processing process greatly affects quality, the chromate process is expected to solve the problem of worker inexperience or not being caught by humans at the manufacturing process site, which predicts accurate quality. Furthermore, several attempts using chromate process \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Precision** & **Sensitivity** & **Specificity** & **F1-Score** & **AUC** \\ \hline CNN & 0.952 & 0.969 & 0.971 & 0.963 & 0.993 \\ AE & 0.964 & 0.974 & 0.981 & 0.951 & 0.978 \\ AnoGAN & 0.928 & 0.983 & 0.980 & 0.931 & 0.964 \\ Fv-AD & **0.978** & **0.981** & **0.982** & **0.971** & **0.997** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance. Figure 13: Localization images. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Precision** & **Sensitivity** & **Specificity** & **F1-Score** & **AUC** \\ \hline \(ziz\) & 0.923 & 0.957 & 0.966 & 0.927 & 0.942 \\ \(izi\) & 0.908 & 0.921 & 0.951 & 0.894 & 0.931 \\ \(izi_{f}\) & **0.978** & **0.981** & **0.982** & **0.971** & **0.997** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Encoder training performance. data are considered meaningful among the various unsupervised learning techniques.