Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4467
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dc.contributor.authorDutta, Pritha-
dc.contributor.authorNayak, Rashmiranjan-
dc.contributor.authorPati, Umesh Chandra-
dc.date.accessioned2024-03-12T10:00:01Z-
dc.date.available2024-03-12T10:00:01Z-
dc.date.issued2024-03-
dc.identifier.citationInternational Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), C. V. Raman Global University, Bhubaneswar, 1-2 March 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4467-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThe process of automatically finding and localizing the available anomalies (or defects) in the images of the products is known as Image Anomaly Detection and Localization (IADL). The IADL improves the efficiency of industrial quality inspection and ensures the desired quality level of the final products. Further, most of the supervised techniques are unsuitable for the IADL due to inherent data imbalance and ambiguity associated with the anomalies. Hence, this paper investigates key deep learning-based unsupervised IADL methods, such as Patch Dis- tribution Modeling (PaDiM), Student-Teacher Feature Pyramid Matching (STFPM), Conditional Normalizing Flow (Cflow), Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection (DFM), and Deep Feature Kernel Density Estimation (DFKDE), for three publicly available bench- marked industrial defect detection datasets: MVTec AD, Visa and BTAD. Finally, a comparative analysis using both quantitative and qualitative performance metrics at the image as well as pixel levels is performed to draw some insightful conclusions.en_US
dc.subjectCFlowen_US
dc.subjectDeep learningen_US
dc.subjectDFKDEen_US
dc.subjectDFMen_US
dc.subjectImage anomaly detection and localizationen_US
dc.subjectIndusrtial quality assuranceen_US
dc.subjectPaDiMen_US
dc.subjectSTFPMen_US
dc.titleExploring Deep Learning-based Unsupervised Image Anomaly Detection and Localization Methods for Industrial Quality Assuranceen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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