Ai-Driven Satellite Imagery Analysis: Identifying Potential Enemy Action Through Military Object Detection

Effective military surveillance relies on robust object detection to identify potential threats in satellite imagery. This study advances situational awareness by developing an enhanced object detection framework for military applications. We leverage a state-of-the-art deep learning model, adapted for aerial imagery, to detect military objects with high accuracy. Our approach introduces a novel feature extraction mechanism in the model's architecture, improving localization and object representation. Using a large-scale aerial imagery dataset, we achieve a mean Average Precision (mAP) of 69.3%, outperforming established benchmarks. This improvement demonstrates the potential of our method to enhance real-time intelligence analysis. Future work will focus on refining the model for diverse operational scenarios and exploring real-world deployment. Our findings underscore the value of tailored AI solutions for military surveillance, offering improved precision and reliability in dynamic environments.