Smoke image detection is an important means for early detection of fires. Aiming at the problems of low robustness and low detection rate of traditional LBP (Local Binary Patterns) feature and Gabor feature fusion algorithms, a TDFF (Triple Multi Feature Local Binary Patterns and Derivative Gabor Feature Fusion) smoke detection algorithm is proposed. First, the T-MFLBP(Triple Multi Feature Local Binary Patterns) algorithm is used to encode and calculate the different grayscale differences between pixels and the pixels at special positions in the non-uniform mode, which can capture clearer texture features. Second, the first-order partial derivative of the Gaussian kernel function is used to extract Gabor features, so as to optimize the performance of extracting image edge gray information. Finally, the fusion features can be trained to improve the accuracy of the final classification. The experimental results show that the TDFF algorithm has strong robustness, and the detection rate of smoke images is also significantly better than the unimproved traditional algorithm.