Aircraft detection in aerial imagery based on YOLO architectures

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.