Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV
Yazarlar (2)
Ibrahım Shamta
Doç. Dr. Batıkan Erdem DEMİR Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | PLOS ONE (Q2) | ||
| Dergi ISSN | 1932-6203 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2024 |
| Cilt / Sayı / Sayfa | 19 / 3 / 1–20 | DOI | 10.1371/journal.pone.0299058 |
| Makale Linki | http://dx.doi.org/10.1371/journal.pone.0299058 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection. |
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BM Sürdürülebilir Kalkınma Amaçları
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| Web of Science | 50 |