UAV Intrusion Detection using Deep Learning Approaches

Abstract

Every day, there is an upsurge in the number of terrorist attacks carried out by drones. As a result, drone detection has become mandatory. Real-time detection of drones is a very challenging task due to their small size, lightning conditions, and relative viewing angles. In this article, a new UAV dataset is presented to perform drone detection tasks using two deep learning techniques, YOLOv5 and YOLOv8, along with the existing Det-Fly dataset. Implementing the YOLOv5 technique, the mean average precision (mAP) for drone detection on both the Det-Fly and UAV datasets is 97.2% and 94.1%, respectively. Similarly, the corresponding values for the YOLOv8 algorithm are 99.5% and 95.0%, respectively