Improving Visual Defect Detection and Localization in Industrial Thermal Images Using Autoencoders

Improving Visual Defect Detection and Localization in Industrial Thermal Images Using Autoencoders

Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from he...

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Bibliographic Details
Main Authors: Sasha Behrouzi, Marcel Dix, Fatemeh Karampanah, Omer Ates, Nissy Sasidharan, Swati Chandna, Binh Vu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Journal of Imaging
Subjects:
anomaly detection
deep learning
novelty detection
autoencoder
industrial image
thermal image
Online Access:https://www.mdpi.com/2313-433X/9/7/137

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https://www.mdpi.com/2313-433X/9/7/137

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