Aiming at the problem of low detection rate of depth feature based object detection method Faster R-CNN （Faster Region-based Convolutional Neural Network） in flame detection tasks, a color-guided anchoring strategy was proposed. In this strategy, a flame color model was designed to limit the generation of anchors, which means the flame color was used to limit the generation locations of the anchors, thereby reducing the number of initial anchors and improving the computational efficiency. To further improve the computational efficiency of the network, the masked convolution was used to replace the original convolution layer in the region proposal network. Experiments were conducted on BoWFire and Corsician datasets to verify the detection performance of the proposed method. The experimental results show that the proposed method improves detection speed by 10.1% compared to the original Faster R-CNN, has the F-measure of flame detection of 0.87 on BoWFire, and has the accuracy reached 99.33% on Corsician.The proposed method can improve the efficiency of flame detection and can accurately detect flames in images.

Focusing on the issue that the tracking algorithm based on template matching has poor performance in running speed and success rate, a template matching tracking algorithm based on Particle Swarm Optimization (PSO) was proposed. The algorithm took the PSO algorithm as the search strategy of the candidate templates in template matching algorithm, and the target template was updated self-adaptively. Firstly, 30 candidate templates were selected in a search scope and then the individual and global optimal candidate template were selected; secondly, the best candidate template was worked out through the particle swarm optimization and the target is the best one; finally, the target template was updated self-adaptively based on the matching rate of the best candidate template. The theoretical analysis and simulation experiments show that, compared with the tracking algorithm based on template matching and the template matching tracking algorithm based on the rough search and refined by search, the computation of the template matching tracking algorithm based on particle swarm optimization is greatly reduced about 91.1% and 69.8%, and the success rate is 2.02 times and 1.94 times of the primary algorithm. The experiment show that the new algorithm can achieve well real-time tracking and the robustness and accuracy of tracking is greatly improved.

For the numerical simulation of one-dimensional Burgers' equation based on Lattice Boltzmann method, there had been 2-bit and 4-bit models. In this paper, an equilibrium distribution function was constructed by choosing the proper kind of discrete velocity model. And then, using Lattice Bhatnagar-Gross-Krook (LBGK) model, Chapman-Enskog expansion and multiscale technique, a 3-bit Lattice Boltzmann Method (LBM) called D1Q3 model was proposed for the one-dimensional Burgers equation. Some numerical experiments were carried out and the numerical results were in good agreement with analytical solutions, therefore the effectiveness of the new method was verified.