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 (avtor), Bračun, Drago (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: automated visual inspection, deep learning, anomaly detection, unsupervised learning, semi-supervised learning, mixed supervision learning
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: 19 str.
• Številčenje: Vol. 164, art. 104198
• PID: 20.500.12556/RUL-164447
• UDK: 004.9
• ISSN pri članku: 0166-3615
• DOI: 10.1016/j.compind.2024.104198
• COBISS.SI-ID: 212951555
• Datum objave v RUL: 25.10.2024

Gradivo je del revije

• Naslov: Computers in industry
• Skrajšan naslov: Comput. ind.
• Založnik: North-Holland, Elsevier
• ISSN: 0166-3615
• COBISS.SI-ID: 528402

Licence

• Licenca: CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc/4.0/deed.sl
• Opis: Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: avtomatiziran vizualni nadzor, globoko učenje, iskanje anomalij, nenadzorovano učenje, pol-nadzorovano učenje, učenje z mešanim nadzorom

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2–0270
• Naslov: Proizvodni sistemi
• Akronim: PTS

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: HIBRIDNE TEHNOLOGIJE TOVARN PRIHODNOSTI ZA ZELENI PREHOD
• Številka projekta: 3360-24-3113, RDP1
• Naslov: GREENTECH