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5 November 2018 A flame recognition algorithm based on LVQ neural network
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Abstract
Fire has caused great losses to human beings. However, there are many problems in traditional fire detection methods.Considering the instability and high rate of erroneous recognition with these methods ,a flame recognition algorithm based on LVQ neural network is proposed in this paper. The basic characteristics and some information of the flame are analyzed.Moreover,the LVQ neural network technology is used to achieve fire detection.First, the suspicious targets of the image are extracted by flame color features.After the image morphological processing,the circular value is calculated and the interference regions with larger circular degree values is eliminated.Then,the dynamic features of the flame are extracted from the continuous frame. The area of fire will increase gradually and the image shows a continuous increase in high brightness area. The sharp corners of the flame are characterized by elongate and its number changes irregularly.Finally,the structure of the LVQ neural network, the designed of the input and output layers have been concluded. On this basis, a flame recognition algorithm based on LVQ neural network has been designed and a series of fire image experiments have been conducted.The experiment shows that the recognition accuracy of the algorithm reaches 96%.
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Shaojun Zeng, Yanping Chen, Mengting Xu, Zilu Chen, and Hao Yin "A flame recognition algorithm based on LVQ neural network", Proc. SPIE 10816, Advanced Optical Imaging Technologies, 108161K (5 November 2018); https://doi.org/10.1117/12.2501137
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