Aircraft Detection in Aerial Imagery Based on YOLO Architectures

ENG: The study is devoted to determining the most efficient YOLO-based architecture for the task of aircraft detection in high-resolution aerial imagery. A comparative analysis was conducted across YOLO models v8 through v11 under three experimental conditions: using pre-trained (raw) models, fine-tuning the models on a domain-specific dataset, and fine-tuning models to a dataset enhanced through a proposed image preprocessing method. The evaluation considered both accuracy and inference performance metrics. The proposed methodology reduced the false negative rate from 19.5% to 3.2% at a confidence threshold of 0.75, underscoring its effectiveness in enhancing target visibility under challenging imaging conditions such as low contrast or background clutter.