Research On Forest Fire Recognition And Detection Algorithm Based On Deep Learning

Forest fires often bring irreparable damage to ecosystems and human society.In order to reduce the losses caused by forest fires,research on forest fire detection technology has been receiving widespread attention.With the advancement of science and technology,forest fire detection technology has become more and more advanced,from traditional manual detection to sensor detection and infrared detection,but various forest fire detection technologies still have the deficiencies of low reliability and high cost.The hierarchical processing and unsupervised learning modes of deep learning algorithms have the advantages of high accuracy,low cost,and fast speed in processing image data.Therefore,based on image data,this paper applies deep learning algorithms to the field of forest fire detection and is committed to develop an accurate and efficient forest fire identification and detection method.The specific research work of this article:(1)According to the characteristics of the two experiments of forest fire identification and target detection,the experiment environment was configured separately,and the corresponding image database was established;(2)Using data augmentation technology to preprocess the images in the forest fire recognition experiment to achieve the effect of expanding data and feature enhancement;marking the candidate frame of the data set in the target detection experiment and making it into a fixed format;(3)Based on three convolutional neural networks of VGG16,Inception V3 and Res Net50,a forest fire recognition model is established using transfer learning technology.The experimental results show that the forest fire recognition model with Res Net50 as the framework has the best performance and the recognition accuracy rate is up to 99.45 %,and the identification of the test set is completed almost instantly;(4)A forest fire detection model was established based on Faster R-CNN,and the detection effect of the model was gradually optimized by adjusting multiple parameters.The experimental results show that the model can accurately detect the fire area in the image and provide a fire location and burning range for the rescue work.