Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels

Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Fas...

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Bibliographic Details
Main Authors: Vladislav Semenyuk, Ildar Kurmashev, Dmitriy Alyoshin, Liliya Kurmasheva, Vasiliy Serbin, Alessandro Cantelli-Forti
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Modelling
Subjects:
drones
sensor fusion
neural networks
vision transformers
inference
training
Online Access:https://www.mdpi.com/2673-3951/5/4/92
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