Forest Fire Recognition Based on Lightweight Convolutional Neural Network

Zhixiang Li,
Hongbin Jiang,
Qixiang Mei,
Zhao Li,


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.


Forest fire recognition, CNN, MobileNet

Citation Format:
Zhixiang Li, Hongbin Jiang, Qixiang Mei, Zhao Li, "Forest Fire Recognition Based on Lightweight Convolutional Neural Network," Journal of Internet Technology, vol. 23, no. 5 , pp. 1147-1154, Sep. 2022.


  • There are currently no refbacks.

Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Office of Library and Information Services, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 974301, Taiwan, R.O.C.
Tel: +886-3-931-7314  E-mail: