Research And Implementation Of Forest Fire Detection Method Based On UAV Image Acquisition

In recent years,due to the frequent occurrence of forest fires,it has brought huge economic losses to China and even the whole country,and it has also caused tremendous damage to the forest's ecological environment.Therefore,the detection and identification technology of forest fires is important.Traditional forest fire monitoring techniques generally only identify fires that have already occurred,and cannot prevent forest fires in time.In order to effectively prevent forest fires,this paper provides a basis for forest fire prediction and prevention by analyzing the characteristics of forest images.Based on the study of forest image features,a variety of fire feature information was used to establish an improved BP neural network and SVM classifier combined forest fire detection algorithm.This paper combines various forest fire detection techniques at home and abroad to study forest fire identification and prevention algorithms.Before other forest image processing,this paper first preprocesses the forest image,and then compares the median filter,frequency-domain high-pass filter and wavelet filter.It is found that the wavelet filter is better than the median filter and frequency-domain high-pass filter in preserving the image edge details after image noise processing.Through the comparison of multiple segmentation algorithms,RGB color segmentation and k-means cluster segmentation are used to segment the forest fire images and forest soil images.Finally,the Canny operator edge detection technique is used to effectively edge the segmented forest fire image.In this paper,through the research and analysis of forest moisture characteristics,the relationship between the gray value of forest image and soil moisture value is used to determine the humidity value in forest environment,which provides a basis for forest fire prevention.At the same time,it is found that the characteristics of forest fire can be effectively used as the basis for fire identification,and the fire characteristics are extracted and analyzed.Through the effective analysis and multiple tests of forest fire flame characteristics,it is determined that the forest fire flame area,sharp angle,roundness and flicker characteristics can be used as the identification characteristics of forest fire flame.Through the study of BP neural network and SVM classifier,the improved BP neural network and SVM classifier are combined to identify the forest fire image.Through the extraction of forest fire flame features,the forest fire identification network is trained continuously,and the forest fire identification algorithm is finally determined.Through the extraction of forest fire features,the improved BP neural network and SVM support vector machine classifier are used to realize the recognition of forest fire images.The recognition nodel has higher recognition rate and faster forest fire image recognition speed.At the same time,the calculation of the gray value of the image of the forest soil humus layer can effectively predict the forest moisture,and then provide a certain basis for forest fire prevention.