Yazar "Alsamurai, Mustafa Qays Fadhil" seçeneğine göre listele

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    Öğe
    Detection of Animals and humans in forest fires using Yolov8
    (2024) Alsamurai, Mustafa Qays Fadhil; Çevik, Mesut
    - The study uses the YOLOv8 deep learning algorithm to detect fire, smoke, humans, and animals in outdoor images. The importance of forests in protecting the biosphere is emphasized, and forest fires are identified as a major risk to the environment and living beings. The researchers created a custom dataset of outdoor images and manually annotated them. The YOLOv8 model was trained on this dataset, and its overall performance was evaluated, with varying results for different object classes. The study identified areas for improvement in the model's ability to detect small instances of fire and smoke and differentiate between animals and humans. The impact of image quality on the model's performance was also highlighted. Overall, the study provides a comprehensive evaluation of YOLOv8's performance in detecting outdoor objects and identifies areas for improvement.
  • [ X ]
    Öğe
    Development and implementation of YOLOV8-based model for human and animal detection during forest fires
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2023) Alsamurai, Mustafa Qays Fadhil; Çevik, Mesut
    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.