Research On Fire Detection Algorithm Based On Video Image

As one of the major natural disasters,fires bring serious economic losses to the society and threaten the safety of people’s lives and property.Therefore,it is of great significance to improve the accuracy of fire detection.Compared with traditional fire detection technology,video fire detection methods have better performance in recognition accuracy and real-time performance,and become one of the current research hotspots.In the current image-based fire recognition research,the use of support vector machines(SVM)for fire image classification and recognition is widely used,but the resulting models generally have the problem of low accuracy in fire sample recognition.In this paper,particle swarm algorithm and genetic algorithm are combined,and GAPSO algorithm is applied to the optimization process of model parameters in order to obtain better parameters to improve the classification accuracy of the classification model for fire samples and improve the detection efficiency.The main research contents of this paper are as follows:(1)Analyze the commonly used digital image processing technology,process the flame image,and prepare for the subsequent fire detection.Through experimental comparative analysis,this paper uses morphological operations to process the acquired flame area,and selects a median filter method to remove image noise.(2)Compare and analyze mainstream moving target detection methods such as frame difference method,optical flow method,and background subtraction,and select the detection method based on the Gaussian mixture model to obtain the moving target area in the video.At the same time,the color space model is introduced to extract the suspected flame area in the image.Combine both the moving target area and the suspected flame area as the final flame area.(3)Perform feature extraction on the flame area.This paper extracts the static and dynamic characteristics of the flame in the image.The static features include circularity,similarity and texture features,and the dynamic features include area change rate and stroboscopic features.The result of the fusion of the extracted features is used as a fire identification sample for follow-up work.(4)Use support vector machine to build fire image detection model.This paper describes the principles,advantages and disadvantages of mainstream swarm optimization algorithms such as particle swarm algorithm and genetic algorithm.Combining the two algorithms,proposes the application of the fusion of GAPSO algorithm and SVM to fire image detection.This paper verifies the performance of the three algorithms and conducts fire detection experiments.The experimental results show that compared with the traditional PSO-SVM and GA-SVM,GAPSO-SVM can improve the accuracy of fire detection and recognition.