İNSANSIZ HAVA ARAÇLARI İÇİN U-NET TABANLI ARAÇ TESPİT YÖNTEMİ

Year 2022, Volume: 10 Issue: 4, 1141 - 1154, 30.12.2022

Oğuzhan Katar Erkan Duman

https://doi.org/10.21923/jesd.1087477

Abstract

Bilgisayar donanımı teknolojisindeki gelişmelerle birlikte bilgisayar görmesi ve yapay zeka alanlarındaki çalışmalar hız kazanmıştır. Bununla birlikte otonom sistemlerin kullanıldığı alanların sayısı da artmıştır. Bu alanlar arasında günümüz askeri teknolojisinin en önemli parametrelerinden biri olan insansız hava araçları yer almaktadır. İki farklı senaryoyu içeren bu çalışmamızda insansız hava araçlarının görüş yeteneklerini yapay zeka tabanlı olarak geliştirmeyi hedefledik. Senaryo-1 kapsamında ikili anlamsal bölütleme yöntemine uygun U-Net modeli sadece araç objesinin tespitini yapabilmek için insansız hava aracı kamerasıyla çekilen görüntüler yardımıyla eğitilmiştir. Hareketli veya durağan araç tespiti için tasarlanan Senaryo-2 kapsamında, U-Net modeli çok sınıflı anlamsal bölütlemeye uygun olarak eğitilmiştir. Tüm bu eğitim süreçlerinde kamuya açık veri seti kullanılmıştır. Senaryo-1 kapsamında eğitilen model %84,3 ortalama birleşim üzerinden kesişme (mIoU) değerine ulaşırken, Senaryo-2 kapsamında eğitilen model %79,7 mIoU değerine ulaşmıştır. Bu çalışmada yüksek çözünürlüklü görüntülerin model eğitiminde ve test aşamalarında kullanılabilmesi hakkında yaklaşımlar paylaşıldı. Bu tür çalışmaların sahada uygulanması, savunma sanayisinde hassaslığı ve güvenirliği iyileştirmeye yardımcı olabilir.

Keywords

İHA, U-Net, Tespit, Görüntü İşleme, Anlamsal Bölütleme

U-NET BASED CAR DETECTION METHOD FOR UNMANNED AERIAL VEHICLES

Abstract

With the developments in computer hardware technology, studies in the fields of computer vision and artificial intelligence has accelerated. However, the number of areas where autonomous systems are used has also increased. Among these areas are unmanned aerial vehicles, which are one of the most important parameters of today's military technology. In this study, which includes two different scenarios, we aimed to improve the vision capabilities of unmanned aerial vehicles based on artificial intelligence. Within the scope of Scenario-1, the U-Net model suitable for binary semantic segmentation method was trained with the help of images taken by unmanned aerial vehicle camera. Within the scope of Scenario-2, which is designed for moving or stationary vehicle detection, the U-Net model is trained in accordance with multi-class semantic segmentation method. In all these training processes, a publicly available dataset was used. The model trained for Scenario-1 reached mean Intersection over Union (mIoU) value of 84.3%, while the model trained for Scenario-2 reached 79.7% mIoU. In this study, approaches were shared about the use of high-resolution images in model training and testing stages. Applying such studies in the field can help improve precision and reliability in arms industry.

Keywords

UAV, U-Net, Detection, Image Processing, Semantic Segmentation

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