Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

1. 서 론
2. 이상 탐지 알고리즘
    2.1 이상 탐지 알고리즘 개요
    2.2 PaDiM
    2.3 FastFlow & CFlow
    2.4 Reverse Distillation(R.D)
    2.5 DRAEM
    2.6 EfficientAD
3. 실험 결과
    3.1 실험 환경 하드웨어
    3.2 데이터 수집
    3.3 테스트 평가 지표
    3.4 MvTec 데이터 벤치마크
    3.5 실험 결과
    3.6 한계점
4. 결 론
References

• Hanbi Kim(Dagyeom CO. LTD.) | 김한비 (다겸㈜)
• Daeho Seo(Dagyeom CO. LTD.) | 서대호 (다겸㈜) Corresponding author