Machine Learning for Real-Time Target Detection and Tracking in UAVs

Bourhane Khadmiry

Abstract


This paper explores the application of machine learning techniques for real-time target detection and tracking in Unmanned Aerial Vehicles (UAVs). We present a framework that combines deep learning algorithms with advanced image processing techniques to enhance the accuracy and speed of target identification. The study highlights the importance of training models on diverse datasets to improve generalization across various environments and conditions. Experimental results demonstrate the effectiveness of the proposed methods, showcasing their ability to achieve high detection rates and reliable tracking performance. This research contributes to the ongoing advancement of UAV capabilities in surveillance, reconnaissance, and monitoring applications.

Keywords


machine learning, target detection, tracking, UAV

References


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