A Study of Detection and Tracking of Artificial Intelligence in UAVS using Machine-Learning Approach

Abstract

This article explores the development and testing of a system for detecting multi-rotor unmanned aerial vehicles (UAVs), a critical need in sectors focused on safeguarding sensitive structures and preserving privacy. Initially employing computer vision techniques, specifically the Oriented FAST and Rotated BRIEF (ORB) feature detector, the study found its real-world applicability limited, prompting a shift towards a machine-learning based detection method. To enhance the model's accuracy, the Common Objects in Context dataset was supplemented with 1000 UAV samples from the Safe Shore dataset. The system's efficacy was evaluated through four rigorous experiments, encompassing scenarios with a single UAV and multiple drones captured in both static images and video footage against sky backdrops. Achieving a notable detection success rate of 97.3% under optimal conditions, this study demonstrates the potential of integrating advanced machine learning techniques with enriched datasets for reliable UAV detection in diverse operational environments.