Mel-Spektrogram Temsiline Dayalı Hibrit Derin Öğrenme Yaklaşımı ile Drone Sınıflandırması

Year 2025, Volume: 30 Issue: 2, 608 - 620, 31.08.2025

Nida Kumbasar , Rabiye Kılıç , Emin Argun Oral , Yücel Özbek

https://doi.org/10.53433/yyufbed.1609294

Abstract

Teknolojik gelişmelerle birlikte son yıllarda hem sivil hem de askeri sektörde dron kullanımı giderek yaygınlaşmaktadır. Güvenlik tehdidi oluşturan durumlarda uzaktan algılama sinyalleriyle bir dronun varlığını, türünü veya uçuş modunu tespit edip tanımlayabilen teknolojilere ihtiyaç duyulmaktadır. Bu çalışma, Mel-spektrogram gösterimleri kullanılarak farklı uçuş modları altında çeşitli dronlardan gelen radyo frekans (RF) sinyallerinin sınıflandırılmasına dayanmaktadır. Çalışma kapsamında, VGG19 derin öğrenme modeli hem bir sınıflandırıcı hem de SVM sınıflandırıcısı için bir özellik çıkarıcı olarak kullanılmıştır. Öte yandan çalışma, RF sinyallerinin düşük ve yüksek frekans bantlarındaki performansını ayrı ayrı ve birleştirilmiş versiyonlarda karşılaştırmaktadır. Deneysel çalışmalarda elde edilen sonuçlarda, VGG19+SVM hibrit modeli, birleştirilmiş düşük ve yüksek (L+H) frekansların Mel-spektrogramı üzerinde en yüksek performansı göstermiştir. dron varlığının tespit edildiği 2-Sınıf problemde (dron var - dron yok) doğruluk performansları %100, dron tiplerinin sınıflandırıldığı 4-Sınıf problemde (Bebop-AR-Phantom-dron yok) %90.78 ve dron modlarının elde edildiği 10-Sınıf problemde ise %86.6 olarak gerçekleşmiştir.

Keywords

Drone sınıflandırma , Hibrit model , Mel-spektrogram , RF sinyal

Drone Classification with a Hybrid Deep Learning Approach Based on Mel-Spectrogram Representation

Abstract

With technological advancements, the use of drones has become increasingly widespread in both civilian and military sectors in recent years. There is a need for technologies that can detect and identify the presence, type, or flight mode of a drone with remote sensing signals in situations that pose a security threat. This study is based on the classification of radio frequency (RF) signals from various drones under different flight modes using Mel-spectrogram representations. Within the scope of the study, the VGG19 deep learning model is used as both a classifier and a feature extractor for the SVM classifier. On the other hand, the study compares the performance of RF signals in low and high frequency bands separately and in concatenated versions. In the results obtained in the experimental studies, the VGG19+SVM hybrid model showed the highest performance over the Mel-spectrogram of the concatenated low and high (L+H) frequencies. The accuracy performances were 100% in the 2-Class problem where drone presence was detected (drone -no drone), 90.78% in the 4-Class problem where drone types were classification (Bebop-AR-Phantom-no drone), and 86.6% in the 10-Class problem where drone modes were obtained.

Keywords

Drone classification , Hybrid model , Mel-spectrogram , RF signal

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