Forest Fire Recognition Based on Lightweight Convolutional Neural Network

In recent years, there have been numerous forest fires, and fire identification technology has become increasingly influential in both academic and industrial fields. At present, most automatic fire alarm systems are limited to identification by sensors such as temperature, smoke, and infrared optics. One of the existing solutions is the method of image feature extraction, which does not need to rely on specific sensors and can be easily embedded in different devices. However, this method has the disadvantage that it is difficult to extract features from image data. To attack this issue, this paper proposes a lightweight convolutional neural network for forest fire recognition. Firstly, three-channel color images of three scenes are constructed as the input of the convolutional neural network, and the initial data are pre-processed and enhanced. Secondly, a deep convolutional neural network with multiple layers of convolution and pooling layers is constructed. Finally, the Softmax function is used to classify the fire recognition scenes. The experimental results show that our approach outperforms these selected techniques in the effectiveness and accuracy.