为了降低火焰状干扰源存在时森林火灾的误报率，提高火灾预警的快速性，根据火焰独特的颜色和纹理特征，提出了以分块的 LBP 直方图特征结合 LPQ 直方图特征的火焰识别算法。首先利用 YCbCr 颜色空间的规则进行颜色检测，得到疑似火焰区域; 再使用 LBP、LPQ 分别从空域、频域提取纹理，图像空域和频域的纹理特征结合后，得到特征向量; 最后将特征向量输入 SVM 分类器进行测试和火焰识别。实验结果表明: 此融合算法鲁棒性强、检测率高，存在火焰状干扰源时，测试集的火焰识别准确率可达 94. 55%; 与深度学习算法对比，该算法在保证较高正确率的同时，预测耗时大幅度减少，预测耗时是 DBN 的1/4、是 CNN 的1/50，提高了火灾预警的快速性，为快速准确的林火预警提供了算法依据。

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 de-signed 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 ro-bust and has a high detection rate． When there is a flame-like interference source，the accuracy of flame iden-tification of the test set can reach 94.55%． Compared with deep learning algorithm，the proposed algorithm can signifi-cantly 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．