AI AND IOT-ENABLED AUTONOMOUS ANTI-DRONE DEFENSE SYSTEM

Abstract: The rapid growth of unmanned aerial vehicles (UAVs), commonly known as drones, has created new opportunities across industries such as surveillance, agriculture, and delivery systems. However, unauthorized or malicious drone usage poses serious security risks to sensitive areas including airports, military installations, and public infrastructure. This paper presents the design and implementation of an autonomous drone detection, tracking, and countermeasure system using an edge computing platform. The proposed system utilizes a deep learning-based object detection model implemented through YOLOv8 to identify drones from real-time video captured by a camera module. The system is deployed on a compact embedded platform based on Raspberry Pi 5 and optimized using the NCNN inference framework to enable efficient edge AI processing. Once a drone is detected, a pan–tilt servo mechanism automatically tracks the object using real-time positional feedback. The system also integrates a directional countermeasure module based on ESP32 and NRF24L01 to demonstrate a 2.4 GHz signal interference mechanism. A web-based monitoring interface provides live video streaming through HTTP Live Streaming generated using FFmpeg, enabling remote system monitoring and event logging. Experimental evaluation demonstrates the feasibility of implementing an intelligent drone monitoring system using low- cost embedded hardware and open-source software technologies.

Keywords: Drone Detection, UAV Monitoring, Edge AI, YOLOv8, NCNN, Raspberry Pi, Computer Vision, Drone Countermeasure, HLS Streaming, Embedded Systems