Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results

Yıl 2025, Cilt: 11 Sayı: 1, 92 - 107, 30.06.2025

Hüseyin Aydilek , Mustafa Yasin Erten

https://doi.org/10.29132/ijpas.1637721

Öz

This study examines the effectiveness of deep learning-based models in the classification and monitoring of fish species. A dataset obtained from the Kaggle platform, containing 31 different fish species, was used to train MobileNetV2, DenseNet121, and VGG19 models. Bayesian optimization was applied to enhance model performance and determine the optimal hyperparameters. The results indicate that models trained with Bayesian optimization achieved significantly higher accuracy compared to those trained with randomly assigned hyperparameters. Additionally, the ensemble learning approach, which combined the outputs of individual models, yielded the best classification performance. This study demonstrates that deep learning techniques serve as a crucial tool for marine ecosystem conservation and sustainable fisheries practices.

Anahtar Kelimeler

Deep learning , Fish species classification , MobileNetV2 , VGG19 , DenseNet121 , Bayesian optimization.

Balık Türü Sınıflandırmasında Derin Öğrenme ve Bayes Optimizasyonu: Model Etkinlik ve Karşılaştırmalı Sonuçlar

Öz

Bu çalışma, derin öğrenme tabanlı modellerin balık türlerinin sınıflandırılması ve izlenmesindeki etkinliğini incelemektedir. Kaggle platformundan elde edilen ve 31 farklı balık türünü içeren veri kümesi kullanılarak MobileNetV2, DenseNet121 ve VGG19 modelleri uygulanmıştır. Model performansını artırmak amacıyla Bayes optimi-zasyonu kullanılmış ve en iyi hiperparametreler belirlenmiştir. Sonuçlar, Bayes opti-mizasyonu uygulanmış modellerin rastgele hiperparametrelerle eğitilmiş modellere kıyasla önemli ölçüde daha yüksek doğruluk oranlarına ulaştığını göstermektedir. Ayrıca, bireysel modellerin çıktılarının birleştirildiği toplu öğrenme yaklaşımı, en iyi sınıflandırma başarımını sağlamıştır. Bu çalışma, derin öğrenme tekniklerinin deniz ekosistemlerinin korunması ve sürdürülebilir balıkçılık uygulamalarında kritik bir araç olduğunu göstermektedir.

Anahtar Kelimeler

Derin öğrenme , Balık türü sınıflandırma , MobilNetV2 , VGG19 , DenseNet121 , Bayes optimizasyonu.

Kaynakça

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