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

Authors : İrem Hatice Doğan, Ozan Arslan, Ayşe Betül Tat, Burhan Şahin, Ege Erberk Uslu, İbrahim Yülüce, Orhan Dağdeviren

Pages : 340-349

Doi:10.28948/ngumuh.1556995

View : 97 | Download : 126

Publication Date : 2025-01-15

Article Type : Research Paper

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.
Keywords : Helikopter Görüntü Sınıflandırma, Nesne Algılama, Evrişimli Sinir Ağları, Veri Arttırma