INTELLIGENT SURVEILLANCE SYSTEM FOR FIRE DETECTION USING YOLOV8

Abstract

This study describes a lightweight deep learning model trained on a self-made image dataset taken inside farms and open areas of the Holy Shrine of Al-Hussainiya in the City of Karbala, Iraq. This dataset includes fire and smoke images taken using a Samsung A52S camera in different weather conditions. The overall goal is to create a fire detection system model that can successfully replace the existing physical sensor-based fire detectors and lessen the issues that come with such fire detectors, including false and delayed triggering. Another goal is to control fires on farms or open areas and prevent crop damage as much as possible. Previous studies were reviewed. Moreover, the architecture of the You Only Look Once version 8 (YOLOv8) model was briefly explained, and the results it achieved were compared with those achieved by previous versions. Then, the proposed system was trained and evaluated with the YOLOv8 large model. Results showed that the proposed system outperformed the rest of the current systems in mAP, which reached 98.5%.

Keywords

• fire detection system
• cnns
• computer vision
• real-time fire detection
• deep learning