Fire Detection Scheme in Tunnels Based on Multi-source Information Fusion

Abstract

Multi-sensor information fusion technology is an effective method for fire detection. However, in the underground road scenario, due to the closed environment and dispersed sensor layout, common fire detection data fusion methods have defects of poor detection timeliness and low accuracy. Therefore, this paper proposes a new fire detection scheme combining BP neural network and D-S evidence theory, and further puts forward a evidence correction method based on exponential entropy. We compare this method with common methods, and the experimental results show that the new method can detect the fire at the earliest in both open fire and smoldering fire scenes of underground roads, which improves the real-time performance and accuracy of fire detection.