Araştırma Makalesi

Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects

Cilt: 5 Sayı: 2 20 Aralık 2024
PDF İndir
EN TR

Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects

Abstract

In industrial manufacturing processes, detection of defects on the surfaces of metal plates supplied from iron and steel main industry manufacturers to be processed by machining and non-machining methods has an important place in estimating the values of the relevant plate such as safety and maintenance cost. With the developing technology and computer vision and deep learning applications finding a place in the industry, it has become possible to detect and classify metal plate surface defects more quickly and effectively with a lower error rate at an advanced technological level. Within the scope of this study, a deep learning model was created by using the TensorFlow library in the Python environment with using NEU Metal Surface Defects Dataset to detect metal plate surface defects. Then as an industrial application, a device prototype developed using Nvidia Jetson Nano and USB Camera, in order to test this model under real conditions.

Keywords

Destekleyen Kurum

Bandırma Onyedi Eylül Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

BAP 22-1010-002

Etik Beyan

Etik kurallara uyum gerektirecek herhangi bir süreç, canlı varlıklar, organizasyonlar, kurumlar üzerinde herhangi bir çalışma gerçekleştirilmemiştir.

Teşekkür

Yürütmekte olduğumuz BAP 22-1010-002 numaralı projemiz boyunca verdikleri destek için Bandırma Onyedi Eylül Üniversitesi Bilimsel Araştırma Projeleri Birimi’ne teşekkürlerimizi sunarız.

Kaynakça

  1. Agarwal M., Gupta S., Biswas K. K., A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant. Sustainable Computing: Informatics and Systems 30, 100473, 2021.
  2. Baldi P., Sadowski P. J., Understanding Dropout. Advances in Neural Information Processing Systems 26, 2013. Barz B., Denzler J., Deep Learning on Small Datasets without Pre-Training using Cosine Loss, In Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2020, pp: 1371-1380.
  3. Bbouzidi S., Hcini G., Jdey I., Drira F., Convolutional Neural Networks and Vision Transformers for Fashion MNIST Classification: A Literature Review. arXiv preprint arXiv:2406.03478, 2024.
  4. Bock S., Weiß M., A Proof of Local Convergence for the Adam Optimizer. 2019 International Joint Conference on Neural Networks (IJCNN), July, 2019, pp: 1-8.
  5. Dung L., Mizukawa M., A Pattern Recognition Neural Network Using Many Sets of Weights and Biases. 2007 International Symposium on Computational Intelligence in Robotics and Automation, 2007, pp: 285–290. Glassmacher S., https://galaxyinferno.com/epochs-iterations-and-batch-size-deep-learning-basics-explained/, 2022, (24 October 2022).
  6. Gulli A., Pal S., Deep Learning with Keras. Packt Publishing Ltd., 2017.
  7. Haji S. H., Abdulazeez A. M., Comparison of Optimization Techniques Based on Gradient Descent Algorithm: A Review. PalArch’s Journal of Archaeology of Egypt / Egyptology 18(4), 2715-2743, 2021.
  8. Helms M., Ault S. V., Mao G., Wang J., An Overview of Google Brain and Its Applications. Proceedings of the 2018 International Conference on Big Data and Education, March, 2018, pp: 72-75.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Derin Öğrenme

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

20 Aralık 2024

Gönderilme Tarihi

9 Temmuz 2024

Kabul Tarihi

18 Kasım 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

APA
Tuncer, C., Közkurt, C., & Kılıçarslan, S. (2024). Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects. Journal of Materials and Mechatronics: A, 5(2), 263-285. https://doi.org/10.55546/jmm.1512549
AMA
1.Tuncer C, Közkurt C, Kılıçarslan S. Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects. J. Mater. Mechat. A. 2024;5(2):263-285. doi:10.55546/jmm.1512549
Chicago
Tuncer, Can, Cemil Közkurt, ve Serhat Kılıçarslan. 2024. “Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects”. Journal of Materials and Mechatronics: A 5 (2): 263-85. https://doi.org/10.55546/jmm.1512549.
EndNote
Tuncer C, Közkurt C, Kılıçarslan S (01 Aralık 2024) Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects. Journal of Materials and Mechatronics: A 5 2 263–285.
IEEE
[1]C. Tuncer, C. Közkurt, ve S. Kılıçarslan, “Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects”, J. Mater. Mechat. A, c. 5, sy 2, ss. 263–285, Ara. 2024, doi: 10.55546/jmm.1512549.
ISNAD
Tuncer, Can - Közkurt, Cemil - Kılıçarslan, Serhat. “Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects”. Journal of Materials and Mechatronics: A 5/2 (01 Aralık 2024): 263-285. https://doi.org/10.55546/jmm.1512549.
JAMA
1.Tuncer C, Közkurt C, Kılıçarslan S. Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects. J. Mater. Mechat. A. 2024;5:263–285.
MLA
Tuncer, Can, vd. “Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects”. Journal of Materials and Mechatronics: A, c. 5, sy 2, Aralık 2024, ss. 263-85, doi:10.55546/jmm.1512549.
Vancouver
1.Can Tuncer, Cemil Közkurt, Serhat Kılıçarslan. Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects. J. Mater. Mechat. A. 01 Aralık 2024;5(2):263-85. doi:10.55546/jmm.1512549