Fine-grained detection and segmentation of civilian aircraft in satellite imagery using YOLOv8

Detection and segmentation of civilian aircraft from satellite imagery has significant importance in applications for air traffic management, surveillance, and defense. Yet, its visual confusions and lack of unification in recognition make it hard. This paper presents that by developing an efficient YOLOv8-based model for aircraft detection, classification, and segmentation within the FAIR1M-2.0 dataset. This proposed methodology involves dataset preprocessing and compatibility adjustments where the backbone used is CSPDarknet53 combining with the C2f module, which provides an efficient multi-scale representation, this happens to be the most critical requirement in distinguishing between among 11 unique categories of aircraft. Including the SAM model helps improve localization precision by achieving more accurate pixel-level segmentation. The present work effectively carried out an accurate classification and described civilian aircraft, containing the enhanced detection and quantification capability appropriate for complex satellite-oriented aircraft analysis. These reasons make the work satisfy the fundamental requirement for very accurate identification and evaluation of aerial images. The approach improves the accuracy and precision of aircraft classification over delicate satellite images, and thus is useful in operations for real-time surveillance and monitoring. Fine-grained classification and segmentation would then be able to effectively capture slight differences between aircraft types, which are now vital to the reliable management of airspaces. This work, therefore sets a good foundation for future development and advancement of high-resolution aerial analysis in diverse operational settings.