Smoke Detection with Dual Convolutional Networks From Infrared Frames

Abstract

In many smoke detection fields, such as fire detection in confined space of aircraft cargo hold, the false alarm rate is still high. In the closed and dark environment of the confined space cabin, the traditional video smoke detection method is difficult to find the fire early because of the limitation of lighting conditions. The advantage of fire detection based on infrared video image is that it does not need lighting conditions and has better performance in dark environment. There is a rapid temperature rise process in the confined space at the beginning of the fire, which is more easily captured by infrared cameras. However, there is little research on infrared frame detection methods in confined space. Therefore, based on the limited space environment of aviation industry, this paper studies the smoke detection problem under the infrared framework, and proposes a high-precision fire and smoke image detection algorithm based on infrared double convolution neural network. By modeling the texture features of neural network and infrared smoke frames, and using video frames as an auxiliary means to increase the number of available training images, the problem of insufficient infrared video data sets is solved. The experimental results show that the detection effect of this method is better than other comparison algorithms in limited space, and the detection false alarm rate is effectively reduced.