AI-Driven Aircraft Defence: Developing Deep Learning CNN Architectures for Autonomous Systems

Abstract: The integration of autonomous systems in aviation presents significant challenges and opportunities for enhancing aircraft defense mechanisms. This project focuses on developing deep learning Convolutional Neural Networks (DCNN) specifically designed for real-time threat detection and classification in aircraft defense systems. By utilizing advanced computer vision techniques, the proposed system aims to identify potential threats, such as unauthorized drones and missile launches, while also addressing cyber threats in an increasingly digital landscape. The architecture will be trained on diverse datasets that encompass various operational scenarios, thereby ensuring robustness and adaptability. This research seeks to establish a framework that not only leverages artificial intelligence to improve situational awareness but also enables rapid response capabilities for autonomous aircraft systems.

Keywords: Autonomous Systems, Aircraft Defence, Deep Learning, Threat Detection, Convolutional Neural Networks (DCNN).