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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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
National Aerospace University «Kharkiv Aviation Institute»
2024-11-01
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| Series: | Радіоелектронні і комп'ютерні системи |
| Assuntos: | |
| Acceso en liña: | http://nti.khai.edu/ojs/index.php/reks/article/view/2653 |
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