The Cutting-Edge YOLO11 for Advanced Aircraft Detection in Synthetic Aperture Radar (SAR) Imagery
Murat Bakırcı, Irem Bayraktar
Abstract
This study presents one of the first evaluations of the YOLO11 algorithm and is the first to apply it for aircraft detection from SAR images. A dataset combining SAR Aircraft Detection Dataset (SADD) images and additional web-mined images was used to train and test the model. YOLO11 achieved significant improvements in detection accuracy, with higher precision, recall, and mAP compared to earlier YOLO iterations such as YOLOv5, YOLOv8, YOLOv9, and YOLOv10. The model exhibited a balanced performance by maintaining competitive inference times while minimizing both false positives and missed detections. These results demonstrate the potential of YOLO11 for real-time applications, particularly in UAV-based surveillance systems, where both speed and accuracy are critical.