Machine Learning Analysis of Drone Classification
The proliferation of drone technology has introduced a variety of models, each with distinct functionalities and applications, presenting a challenge in accurately classifying them. This study investigates the machine learning-based classification of four specific drone models—Inspire, Mavic, Phantom, and No Drone. Historically, drone classification relied on manual inspection and basic pattern recognition techniques, which were limited in accuracy and efficiency due to variability in lighting conditions, image quality, and environmental factors. Traditional systems, constrained by rule-based algorithms and manual feature extraction, often struggled to adapt to the diverse and evolving nature of drone appearances. With advancements in machine learning and computer vision, there is an opportunity to enhance classification performance through automated, data-driven methods. This research addresses the need for a robust classification model capable of accurately differentiating between multiple drone models, including scenarios where no drone is present. The significance of this work lies in its potential to improve drone detection and management systems, facilitating better airspace regulation, security surveillance, and autonomous operations. By applying sophisticated machine learning algorithms to drone classification, this study aims to overcome the limitations of traditional methods and provide a scalable, efficient solution for real-world applications, ultimately contributing to the advancement of drone technology integration and operational reliability.
