It implements a deep autoencoder network and trains input reference image(s) along with various copies automatically generated by data augmentation. The trained ...
An algorithm which detects surface level defects without relying on the availability of defect samples for training is proposed and it can be applied to ...
Mujeeb et al. [147] proposed an unsupervised learning algorithm to detect surface level defects by using a deep auto-encoder network and training input ...
This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal ...
Missing: Augmentation. | Show results with:Augmentation.
May 28, 2024 · This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high- ...
Missing: Augmentation. | Show results with:Augmentation.
Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process.
Unsupervised surface defect detection using deep autoencoders and data augmentation. A Mujeeb, W Dai, M Erdt, A Sourin. 2018 International conference on ...
In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are ...
Nov 22, 2023 · This paper presents a novel unsupervised anomaly detection approach for locating defects based on generative models that learn the distribution of defect-free ...
Oct 26, 2024 · Unsupervised models, such as AutoEncoders and GANs, are transforming surface defect detection, especially in scenarios where labeled data is ...