Enhancing Quality Control: Defect State Classification of Taralli Biscuits with MobileNet-v2 and DenseNet-201
Yazarlar (3)
Doç. Dr. Kemal Tutuncu Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/IDAACS58523.2023.10348851 | ||
| Kongre Adı | IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) | ||
| Kongre Tarihi | 07-09-2023 / 09-09-2023 | ||
| Basıldığı Ülke | Almanya | Basıldığı Şehir | Dortmund |
| Bildiri Linki | https://doi.org/10.1109/idaacs58523.2023.10348851 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| Industrial production and packaging face significant challenges, such as product damage, color changes, and the presence of foreign bodies. These issues greatly impact product quality, profitability, and marketability, leading to increased consumer complaints. To address these concerns, this study presents a novel method for classifying Taralli biscuits using image processing techniques. The research encompasses a dataset of 4,900 images, featuring four types of defects: no defect, defect-shape, defect-object, and defect-color. Leveraging advanced deep learning architectures, including MobileNet-v2 and DenseNet-201, the classification process achieves impressive accuracy rates of 98.71% and 99.39% respectively. By automating the detection of biscuit damage, the proposed method enhances quality control and inspection processes within the food industry. The combination of state-of-the-art image … |
| Anahtar Kelimeler |
| classification | deep learning | MobileNet-v2 | DenseNet-201 | biscuits | defect states |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |
| Google Scholar | 8 |