Smoke Detection Algorithm Based on Improved EfficientDet

Abstract

In the early stage of fire, smoke alarm detection is an important means to prevent fire. And with the continuous construction of monitoring facilities, it is of great significance for the study of smoke video monitoring. In order to meet the detection accuracy and speed of the video, the EfficientDet target detection algorithm was improved. Firstly, the visual analysis of the smoke data set was carried out by clustering method, and the anchor frame parameters in the EfficientDet algorithm were re-calibrated by K-means clustering method. Secondly, the Bi-FPN feature fusion algorithm is improved to reduce the transverse cross-layer connection and increase the longitudinal cross-layer connection, which reduces the calculation of parameters and improves the detection accuracy. Finally, in order to solve the problem of missing detection in small smoke area, a two-channel attention mechanism is added to improve the detection accuracy.