Drone Detection Using Deep Learning: A Benchmark Study

Ahmed Hashem, Thomas Schlechter

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


Since Unmanned Aerial Vehicles (UAVs) became available to the civilian public, it has witnessed dramatic spread and exponential popularity. This escalation gave rise to privacy and security concerns, both on the recreational and institutional levels. Although it is mainly used for leisure and productivity activities, it is evident that UAVs can also be used for malicious purposes. Today, as legislation and law enforcement federations can hardly control every incident, many institutions resort to surveillance systems to prevent hostile drone intrusion. Although drone detection can be carried out using different technologies, such as radar or ultra-sonic, visual detection is arguably the most efficient method. Other than being cheap and readily available, cameras are typically a part of any surveillance system. Moreover, the rise of deep learning and neural network models rendered visual recognition very reliable [9, 21]. In this work, three state-of-the-art object detectors, namely YOLOv4, SSD-MobileNetv1 and SSD-VGG16, are tested and compared to find the best performing detector on our drone data-set of 23,863 collected and annotated images. The main work covers detailed reportage of the results of each model, as well as a comprehensive comparison between them. In terms of accuracy and real-time capability, the best performance was achieved by the SSD-VGG16 model, which scored average precision (AP50) of 90.4%, average recall (AR) of 72.7% and inference speed of 58 frames per second on the NVIDIA Jetson Xavier kit.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Number of pages8
ISBN (Print)9783031253119
Publication statusPublished - 2022
Event18th International Conference on Computer Aided Systems Theory, EUROCAST 2022 - Las Palmas de Gran Canaria, Spain
Duration: 20 Feb 202225 Feb 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13789 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Computer Aided Systems Theory, EUROCAST 2022
CityLas Palmas de Gran Canaria


  • Artificial intelligence
  • Drone detection
  • Neural network
  • Security


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