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Abstract—Currently, there is a concerning rise in fire incidents attributed to environmental warming and human errors. These incidents have severe implications, including environmental degradation, deforestation, disruption of wildlife habitats, air pollution, and water scarcity. To mitigate these issues, the development of accurate and error-free fire detection systems is of utmost importance. Presently, fire detection methods such as YOLOv3, Faster R-CNN, and Siamese Fire Detection are widely employed. This article describes an experiment conducted in Uzbekistan, analyzing different versions of YOLO object detection algorithm, such as YOLOv8 and YOLOv5 for fire detection and evaluating their distinctive features.