Thesis Details
Detection and Recognition of Drone Movement in Video
With the increase in drone availability in recent years, the risk of using drones as a tool for attacks has also increased. Based on these risks, this paper proposes a method for their real-time detection, followed by classification. The proposed approach utilizes the background subtraction method for object detection while using deep learning to classify the detected object. The MOG2 utilizes the Gaussian mixture model method to provide background subtraction, while the YOLOv5 object detection model uses convolutional neural networks for classification. The approach implementation produces a way for drone detection and classification while utilizing the processor, reaching results sufficient for real-time drone detection and classification. The method evaluating 1080p recording using the Intel i5-7600K averaged 16 frames per second while detecting a single object within the frame.
Drone detection, Drone classification, MOG2 method, Computer vision, Deep learning, YOLOv5 model
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Mrázek Vojtěch, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT25109,
author = "Simon Lap\v{s}ansk\'{y}",
type = "Bachelor's thesis",
title = "Detection and Recognition of Drone Movement in Video",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/25109/"
}