Intelligent Detection and Counting of Military Aircraft Using Satellite Imagery and Advanced Algorithms

Abstract

Technological advancements in AI and satellite imaging encourage continuous monitoring on a massive scale. This paper describes a remote military aircraft counting and detection system from satellite images based on “deep learning. For detection in still images and video streams, the system uses the YOLOv8 model. This model uses advanced learning to differentiate between military aircraft and surrounding objects based on their distinct shapes and patterns. This paper uses Flask to design a web interface with user authentication, image upload, and detection based on video streams. The system uses different image preprocessing techniques to boost the quality of images and detection. The system draws bounding boxes and counts aircraft in real time. The system uses a webcam for constant monitoring. The proposed system has a high level of efficiency, reliability, and scalability. The interactive user interface makes the system’s proposed solution more usable and accessible. The proposed system is suitable for defense surveillance, border monitoring, and strategic intelligence systems.