Research On Methods For Identifying Smoke During Early Forest Fires

The forest occupies most of the area on the land where humans live.It can improve the human living environment,play a role in purifying the air and regulating the climate.However,fires not only seriously threaten the safety of forests,but also threaten human survival.Therefore,the research on forest fire video recognition is of great significance.The current rapid development of digital image technology has played an important role in the research of forest fire video recognition.This article focuses on the research of the early smoke in forest fires,combining the increasingly mature unmanned vehicle technology and fire smoke recognition technology to achieve the purpose of full-time and comprehensive forest monitoring.In this paper,an unmanned vehicle is selected as a smoke video acquisition tool,and the acquired video is processed by color space conversion and denoising.After extracting the smoke from the image,frame difference method is used to extract the standard area,and the interference target in the smoke area is eliminated by morphological processing,which is convenient for feature extraction.The feature extraction in this paper is mainly divided into color feature and texture feature extraction.The smoke HSV feature is quantized,and the number of pixels corresponding to the smoke HSV feature is counted as the feature vector of the color;the texture feature is divided into local binary pattern（LBP,Local Binary Pattern）Feature extraction and gray level co-occurrence matrix feature extraction^[1].Finally,this paper uses the Lib SVM image classification technology to test the four kernel functions and compare the test results to determine the radial basis kernel function as the Lib SVM kernel function.The main conclusions are as follows:（1）In the process of extracting smoke areas using the frame difference method,it will cause loss of image details,make the edges of the image not rounded,and do not conform to the real smoke shape,so the extracted smoke areas are morphologically processed.In the experiment,comparing the four methods of opening operation,closing operation,opening operation before closing operation,closing operation before opening operation,it is concluded that the smoke image processed after opening operation and closing operation is more realistic.（2）In this paper,the process of extracting smoke color features is improved.First,the HSV average value of the sample image is calculated,and then the pixels corresponding to this value are extracted,and the number of pixels is used as the feature value.The experiment compares the improved algorithm with the color aggregation vector algorithm.The results show that the improved algorithm has a correct rate of 71.17%and the color aggregation vector algorithm has a correct rate of 54.67%,so the improved algorithm is more suitable for smoke feature extraction in this paper.（3）Using the Lib SVM toolbox,comparing with the four kernel functions,the accuracy of the radial basis kernel function is higher,with an accuracy of 92.5%.In order to improve the classification accuracy rate,four kinds of SVM kernel functions are tested and the test results are compared.The accuracy rate using the radial basis kernel function can reach 92.5%.According to the final result,the combination of unmanned vehicles and smoke recognition technology can recognize the smoke only in the early stage of the fire.The accuracy rate is92.5%,which can play a role in the early prevention and extinguishment of forest fires.