İnce Taneli Hava Aracı Sınıflandırmasında Hiyerarşi-Bilinçli Çapraz Seviye Dikkat Mekanizması

Abstract

Görsel tanıma sistemlerinde ince taneli sınıflandırma problemleri, birbirine benzer alt kategorilerin ayırt edilmesini gerektirmekte ve bu durum özellikle hava aracı tanıma gibi alanlarda kritik zorluklar ortaya çıkarmaktadır. Mevcut yaklaşımlar genellikle hiyerarşik yapıyı göz ardı ederek düz sınıflandırma stratejileri kullanmaktadır. Bu araştırmada, çok seviyeli taksonomi bilgisini etkin biçimde kullanan özgün bir dikkat mekanizması önerilmektedir. Geliştirilen Hiyerarşi-Bilinçli Çapraz Seviye Dikkat (HACLA) mimarisi, üst kategorilerden alt kategorilere bilgi aktarımını dinamik olarak gerçekleştirmektedir. FGVC-Aircraft referans veri kümesi üzerinde yapılan kapsamlı deneyler, önerilen yöntemin varyant düzeyinde %89,31 ± 0,97 doğruluk elde ettiğini ve düz sınıflandırma yaklaşımlarına kıyasla %13,67 iyileşme sağladığını göstermektedir. Elde edilen bulgular, hiyerarşik bilgi aktarımının ince taneli görsel tanıma problemlerinde belirleyici bir rol oynadığını göstermektedir. 

Keywords

• Derin Öğrenme
• Dikkat Mekanizması
• Hava Aracı Tanıma
• Hiyerarşik Öğrenme
• İnce Taneli Görsel Sınıflandırma

Hierarchy-Aware Cross-Level Attention Mechanism for Fine-Grained Aircraft Classification

Abstract

Fine-grained classification problems in visual recognition systems require distinguishing highly similar subcategories, posing critical challenges particularly in domains such as aircraft recognition. Existing approaches typically employ flat classification strategies, disregarding the inherent hierarchical structure. In this study, a novel attention mechanism that effectively leverages multi-level taxonomy information is proposed. The developed Hierarchy-Aware Cross-Level Attention (HACLA) architecture dynamically performs information transfer from parent categories to child categories. Extensive experiments conducted on the FGVC-Aircraft benchmark dataset demonstrate that the proposed method achieves 89,31 ± 0,97% accuracy at the variant level and provides a 13,67% improvement compared to flat classification approaches. The findings indicate that hierarchical information transfer plays a decisive role in fine-grained visual recognition problems. 

Keywords

• Deep Learning
• Attention Mechanism
• Aircraft Recognition
• Hierarchical Learning
• Fine-Grained Visual Classification

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