Advancements in Defense Surveillance: Integrating Deep Learning and Enhanced Materials in CubeSAT Systems

The current paper proposes the design and development of a novel CubeSAT system equipped with a high-resolution multispectral camera and an onboard computer (OBC) employing deep learning algorithms for real-time anomaly detection that effectively track enemy movements. The CubeSAT is intended for defense and space applications, and has shown 87% accuracy and 0.89 ROC value training on YOLOv5 for object detection. Utilizing PEEK (poly-ether-ether-ketone) material, the CubeSAT achieves significant cost and weight reductions while maintaining high strength and temperature resistance. The proposed solution leverages a constellation of polar orbiting satellites to provide comprehensive, real-time monitoring and enhanced defense capabilities.