SkyViewSentinel: A Deep Learning-Driven Military Object Detection Application for Remote-Sensing Satellite Images

Background: In today’s ever-changing world, military forces face significant challenges in maintaining situational awareness and responding swiftly to emerging threats. Traditional aerial surveillance often fails to give timely and thorough intelligence over large areas. Limited coverage, mistakes, and difficulty noticing small changes on the ground hinder military operations. To address these problems, this paper introduces the development of a deep learning-based web application named “SkyViewSentinel”, a solution tailored specifically for military aerial surveillance. Methods: The application framework i.e., SkyViewSentinel has been developed through multiple stages i.e., (i) pre-process the Xview overhead Satellite imagery dataset using Ground Truth refinement and image partitioning method, (ii) employed a SOTA deep model i.e., YOLOv8 as a baseline architecture for the research problem and assessed the performance on experimental dataset, (iii) a series of rigorous experiments have been conducted using deep model and obtained results are reported. (iv) Finally, the trained model has been seamlessly integrated into the web application and develops a comprehensive web-based object detection application. The developed application detects military-based objects from real-time satellite images. Results: The developed application has shown promising results in identifying military objects from satellite images, outperforming other contemporary methods. The designed framework has achieved an overall mAP score of 0.315 for all nine classes of military-based objects. For certain specific classes, detection accuracy exceeds 70%, demonstrating the robustness of the framework. Conclusion: The designed web application enables users to detect military-based objects in the region provided by the user. By harnessing the power of satellite object recognition technology, SkyViewSentinel provides a new way to monitor and understand activities in operational areas.