Detection of Military Aircraft Using YOLO and Transformer-Based Object Detection Models in Complex Environments

Authors : Fatih Şengül, Kemal Adem

Pages : 85-97

Doi:10.17671/gazibtd.1549034

View : 48 | Download : 39

Publication Date : 2025-01-31

Article Type : Research Paper

Abstract :Computer vision and deep learning techniques are widely applied in object detection tasks across various domains, including defense technologies. Accurate and efficient detection of military aircraft plays a critical role in strengthening air defense systems and enabling effective strategic decision-making. This study evaluates the performance of YOLOv7, YOLOv8, and RT-DETR models in detecting military aircraft using a dataset consisting of 19.514 images spanning 43 aircraft models. The dataset incorporates images captured from various angles and diverse backgrounds, such as urban, rural, and coastal areas, ensuring realistic testing conditions. However, class imbalance is observed, with certain aircraft models, such as the F14 and F16, being more represented than others, which may affect model generalization. To address these challenges, hyperparameters were optimized, and performance metrics, including mean Average Precision (mAP) and recall, were analyzed. Experimental results show that YOLOv8 achieved 94% mAP and 88.1% recall, YOLOv7 reached 90.2% mAP and 82.7% recall, while RT-DETR demonstrated consistent performance with 92.7% mAP and 90.4% recall. These findings highlight the strengths and limitations of the evaluated models and provide inferences for improving detection systems in defense applications.
Keywords : savaş uçağı tespiti, YOLOv7, YOLOv8, RT-DETR