Pseudo-Supervised Defect Detection Using Robust Deep Convolutional Autoencoders

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 intermi...

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Bibliographic Details
Published in:Sakarya university journal of computer and information sciences Vol. 5; no. 3; pp. 385 - 403
Main Author: Alpdemir, Mahmut Nedim
Format: Journal Article
Language:English
Published: Sakarya University 31.12.2022
Subjects:
anomaly detection
convolutional neural networks
defect detection
machine learning
robust autoencoders
ISSN:2636-8129, 2636-8129
Online Access:Get full text
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Summary: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.
ISSN:2636-8129
2636-8129
DOI:10.35377/saucis...1196381