Development of a cost-effective optical fiber non-contact object classification system using machine learning techniques
Yazarlar (3)
Doç. Dr. Şekip Esat Hayber Bursa Uludağ Üniversitesi, Türkiye
Doç. Dr. Serkan KESER Kırşehir Ahi Evran Üniversitesi, Türkiye
Öğr. Gör. Yunus GÖRKEM Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Optics and Lasers in Engineering (Q1) | ||
| Dergi ISSN | 0143-8166 Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 198 / 1 / 109529– | DOI | 10.1016/j.optlaseng.2025.109529 |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S0143816625007134 | ||
| UAK Araştırma Alanları |
Mühendislik
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| Özet |
| Material-specific spectral reflectance provides a reliable basis for identification and classification. Based on this principle, we offer a low-cost, three-wavelength, distance-scanning fiber optic system that is ideal for material identification, surface defect inspection, and quality control in confined or difficult-to-access industrial settings. In this study, we developed a compact, cost-effective, optical fiber non-contact object classification (OF-NOC) using three distinct wavelengths. Reflectance data collected from ten objects is used to train and test various machine and deep learning classifiers, including a narrow-layered neural network (NL-NN), a bilayered NN (BL-NN), a trilayered NN (TL-NN), a weighted K-nearest neighbors (WKNN), a support vector machine (SVM), a convolutional neural network (CNN), a gated recurrent unit (GRU), and a long short-term memory (LSTM). The ten objects were restructured into four … |
| Anahtar Kelimeler |
| Deep learning | Machine learning | Neural networks | Non-contact object classification | Photonics | Plastic optical fiber |
Science Direct
Development of a cost-effective optical fiber non-contact object classification system using machine learning techniques
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |