A novel FuseDecode Autoencoder for industrial visual inspection : incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort

Kozamernik, Nejc (author), Bračun, Drago (author)

The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection model featuring incremental learning. Initially, the FuseDecode AE operates in an unsupervised manner on noisy data containing predominantly normal images and a small number of anomalous images. The predictions generated assist experts in distinguishing between normal and anomalous samples. Later, it adapts to weakly labeled datasets by retraining in a semi-supervised manner on normal data augmented with synthetic anomalies. As more real anomalous samples become available, the model further refines its capabilities through mixed-supervision learning on both normal and anomalous samples. Evaluation on a real industrial dataset of coating defects shows the effectiveness of the incremental learning approach. Furthermore, validation on the publicly accessible MVTec AD dataset demonstrates the FuseDecode AE's superiority over other state-of-the-art reconstruction-based models. These findings underscore its generalizability and effectiveness in automated visual inspection tasks, particularly in industrial settings.

• Language: English
• Keywords: automated visual inspection, deep learning, anomaly detection, unsupervised learning, semi-supervised learning, mixed supervision learning, reconstruction-based anomaly detection
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 19 str.
• Numbering: Vol. 164, art. 104198
• PID: 20.500.12556/RUL-164447
• UDC: 004.9
• ISSN on article: 0166-3615
• DOI: 10.1016/j.compind.2024.104198
• COBISS.SI-ID: 212951555
• Publication date in RUL: 25.10.2024

Record is a part of a journal

• Title: Computers in industry
• Shortened title: Comput. ind.
• Publisher: Elsevier
• ISSN: 0166-3615
• COBISS.SI-ID: 528402

Licences

• License: CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
• Link: http://creativecommons.org/licenses/by-nc/4.0/
• Description: A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.

Secondary language

• Language: Slovenian
• Keywords: avtomatiziran vizualni nadzor, globoko učenje, iskanje anomalij, nenadzorovano učenje, polnadzorovano učenje, učenje z mešanim nadzorom

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0270
• Name: Proizvodni sistemi, laserske tehnologije in spajanje materialov

• Funder: EC - European Commission
• Funding programme: NextGenerationEU
• Acronym: GREENTECH