Detection of Drone with Convolutional Neural Network

Abstract

The increasing use of drones is notable in both military operations and various civilian activities. However, the difficulty in detecting these devices has become a concern when it comes to protecting sensitive areas from unauthorized drone flights. Compounding these challenges is the ability of drones to fly at night, adding an extra layer of difficulty to surveillance and information security efforts. This paper explores the use of an expanded CO₂ laser beam, in a laboratory setting, as an illuminator directed at a drone flying in a controlled environment, aiming to capture images in the long-wave infrared (LWIR) spectrum. The acquired images were used to train a convolutional neural network (CNN) using the YOLO (You Only Look Once) architecture. The results demonstrate the feasibility of using this approach to detect drones when illuminated by an energy source.