Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection

Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection

In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD)...

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Bibliographic Details
Main Authors: Wei Huang, Yongjie Li, Zhaonan Xu, Xinwei Yao, Rongchun Wan
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
anomaly detection
pre-trained network
deep SVDD
feature patching
unsupervised learning
Online Access:https://www.mdpi.com/1424-8220/25/1/67
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