Segmentation-enhanced ensemble deep learning for animal species classification

Abstract

In recent years, there has been a significant surge in the applications of artificial intelligence, with remarkable advancements recorded in their predictive and problem-solving capabilities. Image processing technologies have emerged as a pivotal component of this advancement, enabling the development of models capable of detecting fine details with high accuracy and thereby providing critical solutions for areas such as biodiversity conservation. The accurate identification and classification of animal species are of paramount importance for monitoring endangered species, assessing ecosystem health, and planning conservation efforts. However, traditional methods are often time-consuming, costly, and prone to human error, thus heightening the need for more reliable and efficient systems. In this study, a dataset comprising 74 different animal species was utilized. The images in this dataset were first segmented using the SAM 2 model, after which independent models were trained with deep learning architectures such as InceptionV3, Xception, and DenseNet169, and hyperparameter optimization was performed using the Bayesian search method. To further enhance classification performance, a Soft Voting ensemble learning approach was employed, achieving an accuracy rate of approximately 93%. This proposed model has been named The Deep Pet Ensemble. The results indicate that while artificial intelligence and image processing techniques can serve as powerful tools for animal species recognition, they also present a rapid, cost-effective alternative for supporting biodiversity conservation, ecosystem sustainability, and scientific research.

Keywords

• Deep learning
• Pet Image Classification
• Segmentation
• Ensemble Learning
• Artificial Intelligience

Hayvan Türlerinin Sınıflandırılması için Segmentasyon Destekli Topluluk Derin Öğrenme Yaklaşımı

Abstract

Son yıllarda yapay zekâ uygulamalarında önemli bir artış gözlemlenmiş; bu alanda tahmin ve problem çözme yeteneklerinde dikkate değer ilerlemeler kaydedilmiştir. Görüntü işleme teknolojileri, bu gelişimin kilit bileşenlerinden biri olarak öne çıkmakta; yüksek doğrulukla ince detayları tespit edebilen modellerin geliştirilmesini mümkün kılmakta ve biyolojik çeşitliliğin korunması gibi alanlarda kritik çözümler sunmaktadır. Hayvan türlerinin doğru bir şekilde tanımlanması ve sınıflandırılması, nesli tükenmekte olan türlerin izlenmesi, ekosistem sağlığının değerlendirilmesi ve koruma çalışmalarının planlanması açısından büyük önem taşımaktadır. Ancak geleneksel yöntemler genellikle zaman alıcı, maliyetli ve insan hatasına açık olduğundan, daha güvenilir ve verimli sistemlere duyulan ihtiyaç artmaktadır. Bu çalışmada, 74 farklı hayvan türünden oluşan bir veri kümesi kullanılmıştır. Veri kümesindeki görseller öncelikle SAM 2 modeli ile segmentasyona tabi tutulmuş; ardından InceptionV3, Xception ve DenseNet169 gibi derin öğrenme mimarileriyle bağımsız modeller eğitilmiş ve hiperparametre optimizasyonu Bayesyen arama yöntemiyle gerçekleştirilmiştir. Sınıflandırma performansını daha da artırmak amacıyla Soft Voting (Yumuşak Oylama) topluluk öğrenme yaklaşımı uygulanmış ve yaklaşık %93 doğruluk oranı elde edilmiştir. Önerilen bu model "The Deep Pet Ensemble" olarak adlandırılmıştır. Sonuçlar, yapay zekâ ve görüntü işleme tekniklerinin hayvan türlerini tanımada güçlü bir araç olabileceğini; aynı zamanda biyolojik çeşitliliğin korunması, ekosistem sürdürülebilirliği ve bilimsel araştırmalar için hızlı ve maliyet etkin bir alternatif sunduğunu göstermektedir.

Keywords

• Derin Öğrenme
• Evcil hayvan görseli sınıflandırıcısı
• Görüntü segmentasyonu
• Topluluk öğrenme
• Yapay zekâ

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