Research on Flame Image Recognition Technology based on Convolutional Neural Network
In order to improve the accuracy rate,response speed and anti-false alarm ability of convolu-tional neural network(CNN)in the flame image recognition,realize the effective detection of very early flame and ensure the reliability of the flame image detection system,a depth-separable convolutional flame image recognition algorithm using dual-scale denoising was proposed to improve the CNN flame rec-ognition algorithm.The response speed and anti-false alarm experiments of the flame image detector transplanted with this algorithm and the comparison experiment with a flame image detector using neural network algorithm showed that the response speed of the detector to a 5 cm x 5 cm fire disk placed at 15 m away from it was 2.8 seconds;the detector had strong anti-interference ability to strong light source interference and high thermal source interference;the detector using the improved algorithm was superior to the original one in sensitivity,response speed and anti-interference ability.The results show that the flame image detector using the dual-scale denoising depth-separable convolutional flame image recognition algorithm has higher sensitivity,response speed and stronger anti-interference ability to the flame.
convolutional neural network(CNN)flame recognitionimage processingintelligent al-gorithmmodel training
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