SEAD: Segment Element-Based Anomaly Detection

SEAD: Segment Element-Based Anomaly Detection

Anomaly detection in images is crucial for the quality control process in manufacturing. Existing anomaly detection methods have achieved high accuracy on many public datasets, which typically include structural anomalies that affect part of the image. However, in real-world scenarios, in addition t...

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Bibliographic Details
Main Authors: Kosaburo Hattori, Ryuto Ishibashi, Hayata Kaneko, Tomonori Izumi, Lin Meng
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Anomaly detection
logical anomalies
SegGPT
deep learning
Online Access:https://ieeexplore.ieee.org/document/10807229/
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https://ieeexplore.ieee.org/document/10807229/

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