ITM Web Conf.
Volume 43, 2022The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
|Number of page(s)||3|
|Published online||14 March 2022|
A sight on defect detection methods for imbalanced industrial data
CCPS Laboratory, ENSAM, University of Hassan II, Casablanca, Morocco
* Corresponding author. Email: firstname.lastname@example.org
Product defect detection is a challenging task, especially in situations where is difficult and costly to collect defect samples. Which make it quite difficult to apply supervised algorithms as their performances decrease by training the model on imbalanced data. To tackle this problem, researchers used data augmentation and one-class classification to detect defects in industrial areas. In this paper, we list defect detection applications for imbalanced industrial data and we report the benefits and limitation of those methods.
Key words: Defect detection / imbalanced data / data augmentation / one-class classification
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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