Real Time Fire Detection using Deep Convolutional Neural Networks and Long-Short Term Memory in Video Surveillance

Abstract

Abstract: Fire detection remains a significant area of research with relevant, practical safety solutions and is an essential component of a building safety monitoring system. The most typical catastrophe is fire. Negligence on the part of individuals is unquestionably the primary cause. Sensor-based systems that have independently detected temperature and smoke perform the majority of fire detections. In order to construct an automated fire detection system that can handle complex real-world fire occurrences, an effective monitoring system is an extremely important development. An early discovery framework is important to keep fires from fanning crazy. In image classification and other computer vision tasks, convolutional neural networks (CNNs) have demonstrated cutting-edge performance. The main issue with CNN-based fire recognition frameworks is their execution progressively observation organizations. In order to eliminate false fire alarms, we propose a computationally efficient CNN architecture for fire detection by comprehending the scene and employing long short-term memory. The input received from the CNN model is used to represent the genuine fire. This framework is built utilizing highlight map determination calculation and fire limitation calculation for fire recognition, confinement and semantic comprehension of the scene and discovery of the fire is gathered which is essentially because of its expanded profundity with effectiveness and precision by thinking about the particular qualities of the issue of interest and the assortment of fire information.