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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2025 / Cilt: 14 Sayı: 1

Deep learning classification and object detection in helicopter images: Performance analysis of GoogleNet, AlexNet and YOLOv9c architectures

Helikopter görüntülerinde derin öğrenme ile sınıflandırma ve nesne tespiti: GoogleNet, AlexNet ve YOLOv9c mimarilerinin performans analizi

İrem Hatice Doğan(İNÖNÜ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0001-5987-391X
Ozan Arslan(İNÖNÜ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0004-7889-4224
Ayşe Betül Tat(İNÖNÜ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0004-3101-2837
Burhan Şahin(Türk Havacılık ve Uzay Sanayii)0000-0002-8777-7925
Ege Erberk Uslu(EGE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-9119-8574
İbrahim Yülüce(EGE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-3652-7184
Orhan Dağdeviren(EGE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-8789-5086
Pages
340–349
Publication Date
2025-01-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings indicate that CNN architectures and YOLO are effective for helicopter image classification and detection, highlighting their potential applications in military, search and rescue, and civil aviation contexts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Helicopter imaging classification and detection are crucial for autonomous navigation, military operations, search and rescue, and civil aviation management.
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Key Findings & Data:Twenty-four helicopter classes were trained using GoogleNet and AlexNet architectures, while the YOLOv9c model was employed for object detection.
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Conclusion & Impact:These findings indicate that CNN architectures and YOLO are effective for helicopter image classification and detection, highlighting their potential applications in military, search and rescue, and civil aviation contexts.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Helikopter Görüntü Sınıflandırma · Nesne Algılama · Evrişimli Sinir Ağları

Abstract

Helicopter imaging classification and detection are crucial for autonomous navigation, military operations, search and rescue, and civil aviation management. This study utilized two helicopter image datasets, applying data augmentation techniques such as random resizing, cutting, horizontal rotation, rotation, and color adjustments, along with histogram equalization for contrast enhancement. Twenty-four helicopter classes were trained using GoogleNet and AlexNet architectures, while the YOLOv9c model was employed for object detection. The results revealed that the GoogleNet classification model achieved an 81% F1 score, and AlexNet reached 73%. In contrast, the YOLOv9c model demonstrated an average mean Average Precision (mAP) of 87%. These findings indicate that CNN architectures and YOLO are effective for helicopter image classification and detection, highlighting their potential applications in military, search and rescue, and civil aviation contexts.

Özet

Helikopter görüntülerinin sınıflandırılması ve tespiti, otonom navigasyon sistemlerinin, askeri operasyonların, arama kurtarma görevlerinin ve sivil havacılık yönetiminin önemli bileşenlerindendir. Bu çalışmada helikopter görüntüleri için iki farklı veri seti kullanılmıştır. Sınıflandırma için rastgele yeniden boyutlandırma, kesme, yatay döndürme, döndürme ve renk değişiklikleri gibi veri artırma teknikleri uygulanmıştır. Görüntülerin kontrastı da histogram eşitleme yöntemi kullanılarak yeniden düzenlenmiştir. 24 sınıf helikopter veri seti GoogleNet ve AlexNet mimarileri kullanılarak eğitilmiştir. Nesne tespiti için YOLOv9c mimarisi kullanılmıştır. Deneysel sonuçlar, GoogleNet tabanlı sınıflandırma modelinin test setinde %81 F-1 skoru elde ettiğini ve AlexNet modelinde genel F1 skorunun %73 olduğunu göstermektedir. YOLOv9c modeli ise ortalama %87 mAP oranları elde etmiştir. Bu sonuçlar, bir tür derin öğrenme modeli olan CNN mimarilerinin ve YOLO nesne tespitinin helikopter görüntüsü sınıflandırma ve tespitinde iyi olduğunu gösteriyor. Çalışma, helikopter ve bileşenlerini tespit etme ve sınıflandırmada iyi performans gösteren modellerin askeri, arama kurtarma ve sivil havacılık dahil olmak üzere çeşitli alanlarda kullanılabileceğini göstermiştir.

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Cite This Article

Doğan, İ. H., Arslan, O., Tat, A. B., Şahin, B., Uslu, E. E., Yülüce, İ., Dağdeviren, O., *, E. E. U. (2025). Deep learning classification and object detection in helicopter images: Performance analysis of GoogleNet, AlexNet and YOLOv9c architectures. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 1, 340–349. https://doi.org/10.28948/ngumuh.1556995

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