Intelligent Flame Detection Based on Principal Component Analysis and Support Vector Machine

Abstract

Fire prevention and control had significant meaning for public safety and social development. To realize automatic monitoring of compartment fire, this paper proposed an intelligent indoor fire detection method based on infrared thermal image. The first step in the process was to locate and detect suspicious areas in the infrared image. Then the Principal Component Analysis method was utilized to extract features and reduce the dimension of feature. Finally, a Support Vector Machine classifier was designed and trained to distinguish a potential flame from a fire and a light. Compared with k-nearest neighbor (KNN) classifier, Random Forest(RF) classifier, and Logical Regression(LR) classifier, SVM classifier had better performance. The accuracy rate of SVM classifier in the test set was 99.97%, and the flame recall rate by SVM was 99.996%. Experimental results demonstrated that the flame detection method proposed in this paper had significant detection effect and good application prospects.