Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermittent anomalies that happen to clutter a relatively consistent background image. In this paper, we illustrate the use of Robust Deep Convolutional Autoencoders (RDCAE) for defect detection, via a pseudo-supervised training process. Our method introduces synthetic simulated defects (or structured noise) to the training process, that alleviates the scarcity of true (real-life) anomalous samples. As such, we offer a pseudo-supervised training process to devise a well-defined mechanism for deciding that the defect-normal discrimination capability of the autoencoders has reached to an acceptable point at training time. The experiment results illustrate that pseudo supervised Robust Deep Convolutional Autoencoders are very effective in identifying surface defects in an efficient way, compared to state of the art anomaly detection methods.
robust autoencoders anomaly detection defect detection machine learning convolutional neural networks
TUBİTAK BİLGEM
This work was partially supported by TUBİTAK BİLGEM we are grateful for that support.
Primary Language | English |
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Subjects | Artificial Intelligence, Computer Software |
Journal Section | Articles |
Authors | |
Publication Date | December 31, 2022 |
Submission Date | October 29, 2022 |
Acceptance Date | November 22, 2022 |
Published in Issue | Year 2022Volume: 5 Issue: 3 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License