Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1916
Title: | Steel Surface Defect Classification Via Deep Learning | Authors: | Tunal Mustafa Mert Yildiz Ahmet Çakar Tuna |
Keywords: | Deep Learning Image Classification Inception V3 NEU-DET Quality Control ResNet152 V2 Steel Surface Defect VGG19 Xception |
Publisher: | IEEE | Source: | Tunali, M. M., Yildiz, A., & Cakar, T. (2022). Steel Surface Defect Classification Via Deep Learning. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.9919470 | Abstract: | Deep learning and image processing methods have taken place in many parts of our lives, as well as in the quality control stages of production lines. The aim of this study is to train and use a deep learning model to improve quality management using limited data and computing power. To achieve that, deep learning for quality control models were trained by classifying six different steel surface defect images in the NEU-DET dataset. Xception, ResNetV2 152, VGG19 and InceptionV3 architectures were used to train the model. High accuracy was obtained with both Xception and ResNetV2 152. © 2022 IEEE. | URI: | https://hdl.handle.net/20.500.11779/1916 https://doi.org/10.1109/UBMK55850.2022.9919470 |
ISBN: | 9781670000000 |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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File | Description | Size | Format | |
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Steel_Surface_Defect_Classification_Via_Deep_Learning.pdf | Full Text - Article | 667.2 kB | Adobe PDF | View/Open |
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