Journal
ADVANCED ENGINEERING INFORMATICS
Volume 48, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2021.101272
Keywords
Anomaly detection; Defect inspection; Autoencoders; Machine vision
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This paper evaluates the unsupervised autoencoder learning method for automated defect detection in manufacturing, and proposes a new CAE model with regularizations that significantly improves the detection performance based on the center of defect samples.
In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) model only aims at minimizing the reconstruction errors, and makes the representative features widely spread. The proposed CAE in this study incorporates a regularization that improves the feature distribution of defect-free samples within a tight range. It makes the representative feature vectors of all training samples as close as possible to the mean feature vector so that a defect sample in the evaluation stage can generate a distinct distance from the trained center of defect-free samples. The proposed CAE model with regularizations has been tested on a variety of material surfaces, including textural and patterned surfaces in images. The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.
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