Mineral Görüntülerinin Sınıflandırılmasında Derin Öğrenme Mimarilerinin Karşılaştırmalı Analizi ve Performans Optimizasyonu

Öz

Minerallerin doğru ve hızlı bir şekilde sınıflandırılması; jeoloji, madencilik ve endüstriyel uygulamalar açısından büyük önem taşımaktadır. Geleneksel yöntemler çoğunlukla uzman bilgi ve deneyimine dayalı olup zaman alıcı ve öznel sonuçlar üretebilmektedir. Bu çalışmada, mineral görüntülerinin otomatik sınıflandırılması amacıyla derin öğrenme tabanlı bir yaklaşım önerilmiş ve farklı model mimarilerinin performansları karşılaştırmalı olarak analiz edilmiştir. Çalışmada, 15 farklı mineral sınıfına ait toplam 40.354 görüntü içeren Mineral Photos veri seti kullanılmıştır. Deneysel süreçte, ResNet50, EfficientNet-B0, MobileNetV3-Large, DenseNet121 ve Vision Transformer (ViT-B/16) olmak üzere beş farklı önceden eğitilmiş model değerlendirilmiştir. Tüm modeller aynı eğitim protokolü altında 5-katlı çapraz doğrulama yöntemi ile test edilmiştir. İlk aşama sonuçlarına göre EfficientNet-B0 modeli %74.39 doğruluk ile en yüksek performansı elde ederken, ResNet50 (%71.37) ve DenseNet121 (%70.13) modelleri rekabetçi sonuçlar sunmuştur. Buna karşılık ViT-B/16 modeli %48.61 doğruluk ile diğer modellere kıyasla daha düşük performans göstermiştir. İkinci aşamada, EfficientNet tabanlı modeller üzerinde daha gelişmiş deneyler gerçekleştirilmiş ve EfficientNet-V2-S modelinin %83.14 ortalama doğruluk değeri ile daha yüksek ve daha kararlı bir performans sergilediği belirlenmiştir. Ayrıca model, precision, recall ve F1-skoru metriklerinde de dengeli sonuçlar üretmiştir. Elde edilen bulgular, konvolüsyonel sinir ağlarının mineral görüntü sınıflandırma probleminde transformer tabanlı modellere kıyasla daha etkili olduğunu göstermektedir.

Anahtar Kelimeler

• Mineral sınıflandırma
• derin öğrenme
• görüntü işleme
• transfer öğrenme
• konvolüsyonel sinir ağları

Comparative Analysis of Deep Learning Architectures and Performance Optimization in Mineral Image Classification

Öz

Accurate and rapid classification of minerals is of great importance in geology, mining, and industrial applications. Traditional methods are often based on expert knowledge and experience, and can produce time-consuming and subjective results. In this study, a deep learning-based approach is proposed for the automatic classification of mineral images, and the performance of different model architectures is analyzed comparatively. The Mineral Photos dataset, containing a total of 40,354 images belonging to 15 different mineral classes, was used in the study. In the experimental process, five different pre-trained models were evaluated: ResNet50, EfficientNet-B0, MobileNetV3-Large, DenseNet121, and Vision Transformer (ViT-B/16). All models were tested using a 5-fold cross-validation method under the same training protocol. According to the first stage results, the EfficientNet-B0 model achieved the highest performance with 74.39% accuracy, while the ResNet50 (71.37%) and DenseNet121 (70.13%) models presented competitive results. In contrast, the ViT-B/16 model showed lower performance compared to other models with an accuracy of 48.61%. In the second stage, more advanced experiments were conducted on EfficientNet-based models, and it was determined that the EfficientNet-V2-S model exhibited higher and more stable performance with an average accuracy of 83.14%. Furthermore, the model produced balanced results in precision, recall, and F1-score metrics. The findings indicate that convolutional neural networks are more effective than transformer-based models in the mineral image classification problem.

Anahtar Kelimeler

• Mineral classification
• deep learning
• image processing
• transfer learning
• convolutional neural networks

Kaynakça

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