Detection of Fire and Smoke from Video Sequences

Abstract

Since the introduction of deep neural networks in object detection, fire and smoke has been in the focus of many researchers. The recent state of the art Convolution Neural Network (CNN) based architectures provide more than 95% fire-smoke detection accuracy but in controlled environments, e.g., a fire in server rooms or production lines. Fire burns differently in certain environments; wildfires and domestic fire have distinctive characteristics and burning patterns. A fire detection system should be strong enough to better generalize different fire burning patterns. Training such a system needs a massive amount of annotated data describing unique fire patterns. In this research, a deep neural network-based fire and smoke detection system will be proposed. This network's primary focus will be consuming less training data for better generalization to achieve high accuracy with a low false-positive rate. The results have been reported using the ROC, accuracy, false positive rates