Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Abstract

Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.