Object Detection in Unmanned Aerial Vehicle Camera Stream Using Deep Neural Network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146264" target="_blank" >RIV/00216305:26220/22:PU146264 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/xpl/conhome/1800005/all-proceedings" target="_blank" >https://ieeexplore.ieee.org/xpl/conhome/1800005/all-proceedings</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943463" target="_blank" >10.1109/ICUMT57764.2022.9943463</a>

Result language
angličtina
Original language name
Object Detection in Unmanned Aerial Vehicle Camera Stream Using Deep Neural Network
Original language description
Nowadays, the world is experiencing an increasing boom in applications of artificial intelligence, especially deep learning. This is more and more used in many areas such as industry, medicine and security systems, etc. This article deals with object detection from Unmanned Aerial Vehicle (UAV) perspective. The whole system uses one camera, which is suitably positioned on the UAV to capture the scene. Image processing and subsequent object detection using the YOLOv4 model are performed on the Jetson Nano device. The device itself is relatively powerful, but to save the computing power of the device, the YOLOv4 neural network model was modified. The YOLOv4 model was trained on our own dataset. This training set was created specifically for UAV applications. The result of this work is a learned YOLOv4 neural network model designed for UAVs with regard to the used training set. The modified network model is also able to run in real-time and save computing power for possibly other UAV operations. All materials, dataset and scripts used in this work, are available at https://github.com/KicoSVK/object-detection-in-uav-using-yolov4.
Czech name
—
Czech description
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Project
<a href="/en/project/FW01010548" target="_blank" >FW01010548: Detection, identification and tracking of objects from UAV by machine vision</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3503-9866-3
ISSN
2157-023X
e-ISSN
—
Number of pages
5
Pages from-to
80-84
Publisher name
IEEE
Place of publication
Valencia, Spain
Event location
Valencia, Spain
Event date
Oct 11, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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