Indoor Fire Detection Algorithm Based on Second-Order Exponential Smoothing and Information Fusion

Abstract

With the increasing complexity of building structures and interior materials, the danger of indoor fires has become more severe. It is effective to improve the accuracy and timeliness of fire-sensing devices in order to reduce the harm caused by fires. This paper focuses on the temporal characteristics of sensor information, creatively introducing second-order exponential smoothing into the information fusion algorithm. The RNN structure is used to fit the formula and adaptively trained with various types of fire data. Experimental results show that the proposed algorithm achieves an accuracy of 98% in fire recognition, significantly improving the accuracy of fire recognition. To avoid the issue of imbalanced positive and negative samples, this paper comprehensively evaluates parameters such as F1-score and Matthews correlation coefficient (MCC). The achieved scores are 0.97 and 0.95, respectively, indicating the algorithm’s good performance in detecting the presence or absence of fire. Furthermore, the proposed algorithm is tested for its alarm time. The experimental results show that the proposed algorithm can timely identify various types of fires and can give an alarm earlier than traditional fire alarms.