Combining unsupervised and supervised deep learning approaches for surface anomaly detection

Domen Rački; Dejan Tomaževič; Danijel Skočaj

doi:10.1117/12.2688559

28 July 2023 Combining unsupervised and supervised deep learning approaches for surface anomaly detection

Domen Rački, Dejan Tomaževič, Danijel Skočaj

Author Affiliations +

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274906 (2023) https://doi.org/10.1117/12.2688559
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

Anomaly detection in an unsupervised manner has become the go-to approach in applications where data labeling proves problematic. However, these approaches aren’t completely unsupervised, since they rely on the weak knowledge of the dataset distribution into anomalous and anomaly-free subsets and typically require post-training threshold calibration in order to perform anomaly detection. Yet, they do not take advantage of available positive samples during training. In contrast, fully supervised approaches have proven to be more accurate and more efficient, however, they require a sufficient number of anomalous images to be labeled on a per-pixel level, which represents a labour-intensive task. In this paper, we propose a new hybrid approach that utilizes the best of both worlds. We use an unsupervised approach to build a model for generating pseudo labels, followed by a supervised approach in order to robustify anomaly detection. Moreover, we extend this approach with an active learning schema, that results in learning with mixed supervision. We achieve several improvements, i.e., the utilization of available positive image samples, improved anomaly detection performance, and the retention of real-time performance. The proposed approach yields results that are comparable to the fully supervised approach, and at the very least, reduces the number of required labeled anomalous samples.

Citation Download Citation

Domen Rački, Dejan Tomaževič, and Danijel Skočaj "Combining unsupervised and supervised deep learning approaches for surface anomaly detection", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274906 (28 July 2023); https://doi.org/10.1117/12.2688559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Statistical modeling

Performance modeling

Inspection

Data modeling

Deep learning

Defect detection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years