Real-time AI Monitoring System for Wildfire using YOLO Model based on Deep-Learning

Recently, a lot of accidents have occurred in which small embers generated in dry weather fail to suppress the initial fire and spread to large wildfires.To solve this problem, finding embers in the mountains at the beginning is important.An automatic detection system is needed because it is difficult for humans to observe large areas directly.This study was conducted to find a highly practical method by proposing an AI-automatic forest wildfire detection system using image analysis.Through this, it is expected to reduce manpower waste drastically, and it is verified that the learning method that improves the limitations of the existing method has a real effect.High performance was achieved in the accuracy rate, recall rate, and mean average precision of object detection prediction, which can be seen as a wildfire detection performance evaluation index.This paper proposes an AI model(YOLO, You Only Looks Once model) method of deep learning to distinguish between ignition smoke and non-ignition smoke.In the future, it is expected that the scale of forest fire can be inferred by calculating the distance between the forest fire occurrence point and the shooting point and the area using the segmentation technique installed in the AI model.