Öznitelik Seviyesinde Füzyon Yaklaşımının Kuruyemiş Tür Sınıflandırılmasında Performans Değerlendirmesi

Önerilen çalışma, derin öğrenme ağ mimarilerinden ResNet50 ve DenseNet201 ağlarının öğrenme aktarımı kapsamında 11 sınıflı kuruyemiş görüntülerinden oluşan veri setinden anlamlı özelliklerin çıkarılmasında kullanılmasını ve elde edilen özellik kümeleri üzerinden karar destek makineleri ile ürünlerin yüksek doğrulukta sınıflandırılmasını araştırmaktadır. Ayrıca çalışma kapsamında özellik seviyesi füzyonu yaklaşımıyla, iki farklı ön eğitimli ağdan elde edilen özelliklerin birleştirilmesi ile oluşturulan yeni özellik veri kümesinin, sınıflandırılma performansına olan etkisi de incelenmiştir. Sonuçların validasyonu için deneyler 5 katlı çapraz doğrulama tekniği kapsamında gerçekleştirilmiştir. Sınıflandırma sonuçları incelendiğinde, ResNet50 ve DenseNet201, Füzyon mimarileri kullanılarak çıkarılan özelliklerin doğrusal çekirdekli karar destek makineleri ile sınıflandırılması neticesinde sırasıyla %97,86, %98,09 ve %98,68 sınıflandırma doğrulukları elde edilmiştir.

Anahtar Kelimeler:

Sınıflandırma, DenseNet201, ResNet50, Özellik Çıkarımı., Kuruyemiş

Performance Evaluation of the Decision Level Fusion in Dried-Nut Species Classification

The proposed study investigates the use of ResNet50 and DenseNet201 networks, which are deep learning network architectures, to feature extraction from the dataset consisting of 11-class dried-nuts images within the scope of transfer learning and to classify products with high accuracy with support vector machines over the obtained feature sets. In addition, the effect of the new feature dataset created by combining the features obtained from two different pre-trained networks with the feature-level fusion approach on the classification performance was also examined within the scope of the study. For the validation of the results, the experiments were carried out under the 5-fold cross-validation technique. When the classification results are examined, classification accuracies of 97.86%, 98.09% and 98,68% were obtained, respectively, as a result of the classification of the extracted features using the ResNet50, DenseNet201 and Fusion architectures with linear core support vector machines. When the feature-level fusion approach was applied, it was observed that the classification accuracy increased to 98.68%.

Keywords:

Dried-Nuts, Classification, Feature Extraction., DenseNet201, ResNet50,

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