“…Kim and Kim [17] converted the color space format of fire video images, trained the deformation convolution network (DCN) on the image set, and acquired the flame features that adapt to geometric changes, thereby improving the fire recognition effect. Considering the difference between fire source and interference source, Singh et al [18] updated the detection algorithm for the aircraft fire detection system, established a recurrent neural network (RNN) based on long short-term memory (LSTM) and realtime dynamic information, and connected the measured signals into a time series of features to train the established network. Sayyed et al [19] extracted multiple features of the flame, including chroma, area change rate, circularity, number of sharp corners, and centroid displacement; After segmenting the fire images, they created a fast fire recognition model based on time smoothing and logarithmic regression, and tested the model on self-made flame videos.…”