| Title |
Anomaly Detection to Distinguish Structural Differences of Manufacturing Images |
| Authors |
이청엽(Chungyup Lee) ; 김민종(Minjong Kim) |
| DOI |
https://doi.org/10.5573/ieie.2021.58.11.57 |
| Keywords |
Anomaly detection; U-Net; GAN; Autoencoder; SSIM |
| Abstract |
Due to the development of manufacturing technology, it is increasingly difficult to gather data flaws in the manufacturing process, leading to imbalanced data. Therefore, Autoencoder and GAN-based unsupervised anomaly detections that solve the problem of imbalanced data are in the spotlight. However, the Autoencoder and GAN-based models are not only unstable during training, but also typically use L1 and L2 loss functions for a per-pixel error, making it harder to distinguish structural differences between input and reconstruction. Consequently, detecting anomalies in the same class is not suitable in novelty anomaly detection fields. In this work, we present a way to take advantage of SSIM, MS-SSIM loss functions on a U-Net network to identify structural differences between local regions in the same class and improve reconstruction stability while training. Utilizing MVTec dataset, which is specialized for manufacturing processes to detect defects, we compared the performances of L1, SSIM, MS-SSIM on Autoencoder, GAN-based networks. |