Development and implementation of YOLOV8-based model for human and animal detection during forest fires

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dc.contributor.advisorÇevik, Mesut
dc.contributor.authorAlsamurai, Mustafa Qays Fadhil
dc.date.accessioned2023-12-08T13:21:31Z
dc.date.available2023-12-08T13:21:31Z
dc.date.issued2023en_US
dc.date.submitted2023
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractFor 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.en_US
dc.identifier.citationAlsamurai, 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.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4260
dc.identifier.yoktezid828810
dc.institutionauthorAlsamurai, Mustafa Qays Fadhil
dc.language.isoen
dc.publisherAltınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsüen_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectYoloen_US
dc.subjectDeep Learningen_US
dc.subjectCNNen_US
dc.subjectObject Detectionen_US
dc.subjectComputer Visionen_US
dc.titleDevelopment and implementation of YOLOV8-based model for human and animal detection during forest fires
dc.typeMaster Thesis

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