Research on the identification method for the forest fire based on deep learning

There exist some problems of the traditional forest fire recognition technology, such as the complex background of the forest fire images, the weak generalization ability of the image recognition, and the low accuracy, which will lead to false alarm or missing alarm. To solve the problems mentioned above, a multi-level forest fire detection method based on depth learning has been proposed here. First, in order to solve the problem of uneven distribution and small number of samples, the high-quality forest fire samples have been generated with GAN (General Advanced Networks). Second, Adaboost classifier based on the characteristics of HOG (Histogram of Oriented Gridients) has been used to make primary prediction of forest fire area image, and then convolutional neural networks (CNN) and support vector machine (SVM) have been used to carry out the secondary recognition of the fire area. The experimental results show that the forest fire image recognition method proposed in this paper can obtain higher recognition rate and lower false alarm rate after training with fewer samples than other algorithms. At the same time, this method has lower requirements on the hardware environment required for the sample training and recognition, and has obvious advantages over algorithms that require GPU training environment. With this method, the recognition rate of forest fire images can reach 97.6 %, the false alarm rate is 1.4 %, and the missed alarm rate is 1%. The average time for recognizing sample pictures is only 0.7 s, which has high effectiveness and robustness.