딥러닝 기반의 투명 렌즈 이상 탐지 알고리즘 성능 비교 및 적용

• 영문명: Comparison and Application of Deep Learning-Based Anomaly Detection Algorithms for Transparent Lens Defects
• 발행기관: 한국산업경영시스템학회
• 저자명: 김한비(Hanbi Kim) 서대호(Daeho Seo)
• 간행물 정보: 『산업경영시스템학회지』제47권 제1호, 9~19쪽, 전체 11쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2024.03.30

영문 초록

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manu- facturing 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 abnormal- ities 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 ex- cellent 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.

목차

1. 서 론

2. 이상 탐지 알고리즘

3. 실험 결과

4. 결 론

References

키워드

Lens Computer Vision Anomaly Detection Unsupervised Learning