Abstract
Unmanned aerial vehicles (UAVs) have boosted modern living. Tiny, frail high-density targets, low resolution, complicated backgrounds, noise, and poor real-time exposure performance have augmented due to UAV firms. Realtime recognition in high-altitude (HA) infrared thermal images is intricate. Our fresh OSTD-YOLOv8 is a multi-class target recognition tactic to tackle these issues and spot and classify objects on the HIT-UAV dataset. YOLOv8 is an effective advanced object detection model. Our OSTD-YOLOv8 model detects small objects in HA thermal images with 90% precision, 91.5% recall, 90.5% F1-score, 89.1% AP, and 98% mAP. Our unique approach delivers effective object recognition in difficult thermal imaging conditions. Consequently, this distinctive UAV-focused HA, thermal, target detection scheme can be used.
| Original language | English |
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| Title of host publication | 2024 IEEE URUCON, URUCON 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350355383 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE URUCON, URUCON 2024 - Montevideo, Uruguay Duration: 18 Nov 2024 → 20 Nov 2024 |
Publication series
| Name | 2024 IEEE URUCON, URUCON 2024 |
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Conference
| Conference | 2024 IEEE URUCON, URUCON 2024 |
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| Country/Territory | Uruguay |
| City | Montevideo |
| Period | 18/11/24 → 20/11/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- deep learning
- infrared small target
- infrared thermal imaging
- Object detection
- YOLOv8
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems and Management
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Biomedical Engineering
- Electrical and Electronic Engineering
- Health Informatics