改进AFSA算法优化TWSVM的火焰识别方法

为了快速有效地识别火灾火焰图像,提出了一种基于改进人工鱼群算法(IAFSA)的孪生支持向量机(TWSVM)的火焰识别方法.该方法根据RGB-YCbCr混合颜色空间模型中火焰像素的分布特点对火焰图像进行分割,并在此基础上提取火焰图像的相关特征;采用人工鱼群算法(AFSA)搜索TWSVM最优惩罚参数与核参数,并在AFSA算...

Flame Recognition Method Using TWSVM with Improved Artificial Fish Swarm Algorithm

In order to recognize the fire image quickly and effectively, a flame recognition method of Twin Support Vector Machine（TWSVM） based on Improved Artificial Fish Swarm Algorithm（IAFSA） is proposed. Firstly, this method segments the flame image according to the distribution characteristics of flame pixels in RGB-YCbCr mixed color space model, and extracts the relevant features of the flame image on this basis. Secondly, Artificial Fish Swarm Algorithm（AFSA） is used to search the optimal penalty parameter and kernel parameter of TWSVM. In AFSA algorithm, a clustering-based fish initialization method is used to obtain uniform initial fish swarm. At the same time, adaptive parameters are used to adjust the visual range and moving step length of artificial fish swarm. In addition, based on the original three behaviors, two new behaviors are proposed：jumping behavior and eliminating regeneration behavior, which improves the efficiency and accuracy of fish swarm algorithm. Then the extracted flame features are input into TWSVM model as training samples for training. Finally, the samples to be tested are input into TWSVM model for classification and recognition. Experimental results show that compared with VGGNet model of deep convolution neural network, Fast R-CNN algorithm, YOLO algorithm, traditional Support Vector Machine（SVM）, Grid-TWSVM, GA-TWSVM, PSO-TWSVM, FOA-TWSVM, GSO-TWSVM, AFSA-TWSVM, the proposed method of twin support vector machine based on improved artificial fish swarm algorithm effectively improves the accuracy and real-time performance of flame recognition, and solves the problems of TWSVM such as difficult parameter selection in flame recognition and long optimization time of common parameter optimization algorithms.