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

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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Bibliographic Details
Main Authors: Vadym Mishchuk, Herman Fesenko, Vyacheslav Kharchenko
Format: Article
Language:English
Published: National Aerospace University «Kharkiv Aviation Institute» 2024-11-01
Series:Радіоелектронні і комп'ютерні системи
Subjects:
explosive ordnance
object detection
precision
performance
yolo
transformers
Online Access:http://nti.khai.edu/ojs/index.php/reks/article/view/2653
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