Effects of Different Deep Learning Algorithms on Real-Time UAV Detection

Ferda Nur Arıcı; Hediye Orhan

Makale detayları

Effects of Different Deep Learning Algorithms on Real-Time UAV Detection

Yazarlar:

Ferda Nur Arıcı | Hediye Orhan

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.691-706

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321573

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 The aviation industry is constantly developing in our country and around the world. With changing and developing technologies, unmanned aerial vehicles UAVs have begun to be used for different purposes in many sectors. Usage areas of UAVs; Military applications, geological and meteorological research, natural disaster management, agricultural exploration, transportation, earth mapping and three-dimensional modeling can be given as examples. The use of UAVs in the military field in our country is increasing day by day. The most important of these are target detection, target shooting and target tracking. In target shooting, the hit is carried out after the target is detected with the cameras on the UAV. It is very important to identify the target correctly. In order for a UAV to perform its fully autonomous mission, it must detect targets and perform escape maneuvers. For this, the accuracy of target detection must be high and it must work in real time. The aim of this research is to enable a UAV to detect the target in real time during its autonomous mission. In line with the research purpose, artificial intelligence techniques were used to detect the target UAV. The data set created for real-time target detection was trained with different algorithms and the algorithm that gave proportionally high accuracy and high frame per second FPS value was selected. The results obtained were analyzed. Thus, real-time target detection was achieved.

 The aviation industry is constantly developing in our country and around the world. With changing and developing technologies, unmanned aerial vehicles UAVs have begun to be used for different purposes in many sectors. Usage areas of UAVs; Military applications, geological and meteorological research, natural disaster management, agricultural exploration, transportation, earth mapping and three-dimensional modeling can be given as examples. The use of UAVs in the military field in our country is increasing day by day. The most important of these are target detection, target shooting and target tracking. In target shooting, the hit is carried out after the target is detected with the cameras on the UAV. It is very important to identify the target correctly. In order for a UAV to perform its fully autonomous mission, it must detect targets and perform escape maneuvers. For this, the accuracy of target detection must be high and it must work in real time. The aim of this research is to enable a UAV to detect the target in real time during its autonomous mission. In line with the research purpose, artificial intelligence techniques were used to detect the target UAV. The data set created for real-time target detection was trained with different algorithms and the algorithm that gave proportionally high accuracy and high frame per second FPS value was selected. The results obtained were analyzed. Thus, real-time target detection was achieved.

 The aviation industry is constantly developing in our country and around the world. With changing and developing technologies, unmanned aerial vehicles UAVs have begun to be used for different purposes in many sectors. Usage areas of UAVs; Military applications, geological and meteorological research, natural disaster management, agricultural exploration, transportation, earth mapping and three-dimensional modeling can be given as examples. The use of UAVs in the military field in our country is increasing day by day. The most important of these are target detection, target shooting and target tracking. In target shooting, the hit is carried out after the target is detected with the cameras on the UAV. It is very important to identify the target correctly. In order for a UAV to perform its fully autonomous mission, it must detect targets and perform escape maneuvers. For this, the accuracy of target detection must be high and it must work in real time. The aim of this research is to enable a UAV to detect the target in real time during its autonomous mission. In line with the research purpose, artificial intelligence techniques were used to detect the target UAV. The data set created for real-time target detection was trained with different algorithms and the algorithm that gave proportionally high accuracy and high frame per second FPS value was selected. The results obtained were analyzed. Thus, real-time target detection was achieved.

 Ülkemizde ve dünyada havacılık sektörü sürekli olarak gelişmektedir. Değişen ve gelişen teknolojiler ile birlikte insansız hava araçları İHA da pek çok sektörde farklı amaçlar doğrultusunda kullanılmaya başlanmıştır. İHAların kullanım alanlarına; başta askeri uygulamalar olmak üzere, jeolojik ve meteorolojik araştırmalar, doğal afet yönetimi, tarımsal keşifler, ulaştırma, yeryüzünün haritalanması ve üç boyutlu modelleme örnekleri verilebilir. Ülkemizde askeri alanda İHA kullanımı her geçen gün artmaktadır. Bunların başında hedef tespiti, hedef vuruşu ve hedef takibi gelmektedir. Hedef vuruşunda İHA üzerindeki kameralar ile hedef tespit edildikten sonra vuruş gerçekleştirilmektedir. Hedefin doğru tespit edilmesi çok önemlidir. Bir İHAnın tam otonom görevini gerçekleştirebilmesi için hedefleri tespit edip kaçış manevraları uygulaması gerekmektedir. Bunun için hedef tespitinin doğruluk değeri yüksek olmalıdır ve gerçek zamanlı olarak çalışmalıdır. Bu araştırmadaki amaç bir İHAnın otonom görevi sırasında gerçek zamanlı olarak hedefi tespit etmesini sağlamaktır. Araştırma amacı doğrultusunda hedef İHAnın tespiti için yapay zekâ teknikleri kullanılmıştır. Gerçek zamanlı hedef tespiti için oluşturulan veri seti farklı algoritmalar ile eğitilip orantılı olarak yüksek doğruluk değeri ve saniyede yüksek görüntü sayısı frame per second FPS veren algoritma seçilmiştir. Elde edilen sonuçlar analiz edilmiştir. Böylece gerçek zamanlı hedef tespiti yapılmıştır.

Makale detayları

Effects of Different Deep Learning Algorithms on Real-Time UAV Detection

Yazarlar:

Ferda Nur Arıcı | Hediye Orhan

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.691-706

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321573

pISSN:

2147-9526

uav

image processing

deep learning

opencv

artificial intelligenceiha

görüntü işleme

derin öğrenme

opencv

yapay zekâ.

Sayı

Makale detayları

Yazarlar: Ferda Nur Arıcı | Hediye Orhan

Yayıncı: Gazi University

Ana Başlıklar: Bilgi Sistemleri Eğitimi | Bilgi Sistemleri (Diğer)

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.691-706

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001321573

pISSN: 2147-9526

Sayı

Yazarlar:

Ferda Nur Arıcı | Hediye Orhan

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.691-706

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321573

pISSN:

2147-9526