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)...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework. Features are extracted from a pre-trained backbone network on ImageNet, and each extracted feature is split into multiple small patches of appropriate size. This approach effectively captures both macro-structural information and fine-grained local information from the extracted features, enhancing the model’s sensitivity to anomalies. The feature patches are then aggregated and concatenated for further training with the Deep SVDD model. Experimental results on both the MvTec AD and CIFAR-10 datasets demonstrate that our model outperforms current mainstream approaches and provides significant improvements in anomaly detection performance, which is vital for industrial quality assurance and defect detection in real-time manufacturing scenarios.
ISSN:1424-8220

Similar Items