Deep learning models for detection of explosive ordnance using autonomous robotic systems: trade-off between accuracy and real-time processing speed

The study focuses on deep learning models for real-time explosive ordnance detection (EO). This study aimed to evaluate and compare the performance of YOLOv8 and RT-DETR object detection models in terms of accuracy and speed for EO detection via autonomous robotic systems. The objectives are as foll...

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Vydané v:	Radìoelektronnì ì komp'ûternì sistemi (Online) Ročník 2024; číslo 4; s. 99 - 111
Hlavní autori:	Mishchuk, Vadym, Fesenko, Herman, Kharchenko, Vyacheslav
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	National Aerospace University «Kharkiv Aviation Institute 21.11.2024
Predmet:	explosive ordnance object detection precision transformers yolo
ISSN:	1814-4225, 2663-2012
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