Sensors, Vol. 24, Pages 2440: An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder

Sensors, Vol. 24, Pages 2440: An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder
Sensors doi: 10.3390/s24082440
Authors:
Qiying Yang
Rongzuo Guo

Existing industrial image anomaly detection techniques predominantly utilize codecs based on convolutional neural networks (CNNs). However, traditional convolutional autoencoders are limited to local features, struggling to assimilate global feature information. CNNs’ generalizability enables the reconstruction of certain anomalous regions. This is particularly evident when normal and abnormal regions, despite having similar pixel values, contain different semantic information, leading to ineffective anomaly detection. Furthermore, collecting abnormal image samples during actual industrial production poses challenges, often resulting in data imbalance. To mitigate these issues, this study proposes an unsupervised anomaly detection model employing the Vision Transformer (ViT) architecture, incorporating a Transformer structure to understand the global context between image blocks, thereby extracting a superior representation of feature information. It integrates a memory module to catalog normal sample features, both to counteract anomaly reconstruction issues and bolster feature representation, and additionally introduces a coordinate attention (CA) mechanism to intensify focus on image features at both spatial and channel dimensions, minimizing feature info...

Source: Sensors - April 11, 2024 Category: Biotechnology Authors: Qiying Yang Rongzuo Guo Tags: Article Source Type: research