With the increase of human activities and global climate change,the frequency of fire accidents in natural scenic areas is higher than before.In order to detect and handle fires in time,a machine vision based fire detection method is proposed for scenic areas.Firstly,the real time images of the scenic areas are collected through aerial drones and ground video monitoring facilities.Then,the deep learning model of the Convolutional Neural Network(CNN)is used to analyze the images to detect the occurrence of fire.The lightweight neural networks,such as SqueezeNet,ShuffleNet,MobileNet_v2 and ResNet-50,are used for fire identification.In order to simulate the actual situation of Jiuzhaigou Valley Scenic and Historic Interest Area and select the detection algorithm with lower complexity,the identification evaluation across the dataset is also conducted,and it is compared with ResNet-50 of higher complexity.Finally,through the method of image semantic segmentation based on ResNet-18,the classification accuracy of the identification results reached 96%,showing that the method has better robustness.
NETL
NSTL
维普
万方数据
