Forest Fire Recognition Based on Color and Texture Features

Abstract

A flame recognition algorithm based on the partitioned LBP histogram feature combined with the LPQ histogram feature was proposed according to the unique color and texture feature of flame.The algorithm was designed to reduce the false positive rate of forest fire in the presence of flame-like interference source and increase the speed of fire warning.Firstly, the rule in YCbCr color space was used to detect the suspected flame region.Secondly, LBP and LPQ were used to extract the texture from the spatial domain and frequency domain.Then the feature vector was obtained by combining the extracted texture features.Finally, the feature vector was inputted into support vector machine(SVM)for flame recognition.The experimental results show that the algorithm is robust and has a high detection rate.When there is a flame-like interference source, the accuracy of flame identification of the test set can reach 94.55%.Compared with deep learning algorithm, the proposed algorithm can significantly improve the speed of fire warning while ensuring a high accuracy.Its forecasting time is 1/4 of the forecasting time of DBN, and 1/50 of that of CNN.Thus the algorithm provides a basis for fast and accurate forest fire warning.