• 图形图像处理 •

### 改进AFSA算法优化TWSVM的火焰识别方法

1. 物联网技术应用教育部工程研究中心（江南大学物联网工程学院），江苏 无锡 214122
• 出版日期:2021-04-15 发布日期:2021-04-23

### Flame Recognition Method Using TWSVM with Improved Artificial Fish Swarm Algorithm

GAO Yikai, PENG Li, XU Longzhuang

1. Engineering Research Center of Internet of Things Technology Applications（School of IoT Engineering, Jiangnan University）, Ministry of Education, Wuxi, Jiangsu 214122, China
• Online:2021-04-15 Published:2021-04-23

Abstract:

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.