Development and implementation of YOLOV8-based model for human and animal detection during forest fires
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
For the biosphere to be protected, forests are a necessity everywhere in the world. Forest
fires are one of the major risks to life in many parts of the world; they put the environment,
including humans, plants, animals, and even land in danger. The North African &
Mediterranean regions, Amazon & Australia last year suffered greatly from forest fires. To
save lives and property, forest fires must be discovered sooner rather than later. This study
aims to detect fire, smoke, humans, and animals in outdoor images using the YOLOv8 deep
learning algorithm. A custom dataset of outdoor images was created by obtaining images
from various search engines and manually annotating them. The YOLOv8 model was trained
on this dataset and achieved an overall mAP of 0.274, with varying performance for different
object classes. The model had difficulty in detecting small instances of fire and smoke, and
struggled to differentiate between animals and humans in certain cases. The study also
identified the importance of image quality in computer vision and highlighted the impact of
poor image quality on model performance. Overall, the study presents a comprehensive
evaluation of YOLOv8's performance in detecting outdoor objects and identifies areas for
improvement.
Açıklama
Anahtar Kelimeler
Yolo, Deep Learning, CNN, Object Detection, Computer Vision
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Alsamurai, M. Q. F. (2023). Development and implementation of YOLOV8-based model for human and animal detection during forest fires. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
