Indoor Video Flame Detection Based on Lightweight Convolutional Neural Network

At present, all CNN-based fire detection algorithms identify fire by means of a single frame image, all of which demonstrate low accuracy under strong interferences or complex backgrounds such as flickering light or backgrounds with high level of brightness. To increase the accuracy of fire detection, this paper presents a neural network model which combines lightweight CNN with SRU. In this algorithm, the scene content is extracted by CNN and the dynamic characteristics of the flames are extracted from sequential frames. In this paper, Resnet18+SRU (V1-type) and Mobilenets+SRU (V2-type) are proposed. Based on the characteristics of flames at a fixed position within a short period of time, a 3D convolutional layer is added between the Mobilenets and the SRU in the V2-type model, resulting in the V3-type model. Based on a cross validation set containing multiple types of interference in an indoor environment, experiments were conducted to compare the three models proposed in this paper with other models. The experiment results showed that the accuracy of the method proposed in this paper is above 96%, about 25% higher than the accuracy of CNN-based fire alarm via single-frame image, and that the V3-type models with 3D convolutional layer has the highest accuracy and best overall performance.