| Yazarlar (2) |
Emel Soylu
Samsun Üniversitesi, Türkiye |
Öğr. Gör. İbrahim KAYA
Samsun Üniversitesi, Türkiye |
| Özet |
| In this study, we present an advanced electronic component classification system with an exceptional classification accuracy exceeding 99% using state-of-the-art deep learning architectures. We employed EfficientNetV2B3, EfficientNetV2S, EfficientNetB0, InceptionV3, MobileNet, and Vision Transformer (ViT) models for the classification task. The system demonstrates the remarkable potential of these deep learning models in handling complex visual recognition tasks, specifically in the domain of electronic components. Our dataset comprises a diverse set of electronic components, and we meticulously curated and labeled it to ensure high-quality training data. We conducted extensive experiments to fine-tune and optimize the models for the given task, leveraging data augmentation techniques and transfer learning. The high classification accuracy achieved by our system indicates its readiness for real-world deployment, marking a significant step towards advancing automation and efficiency in the electronics industry. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayımlanan tam makale |
| Dergi Adı | Sakarya University Journal of Computer and Information Sciences |
| Dergi ISSN | 2636-8129 Scopus Dergi |
| Dergi Tarandığı Indeksler | TR DİZİN |
| Makale Dili | Türkçe |
| Basım Tarihi | 04-2024 |
| Cilt No | 7 |
| Sayı | 1 |
| Sayfalar | 36 / 45 |
| Doi Numarası | 10.35377/saucis...1391636 |
| Makale Linki | https://doi.org/10.35377/saucis...1391636 |
| Atıf Sayıları |
