An Efficient Backbone for Early Forest Fire Detection Based on Convolutional Neural Networks

Abstract

Forest fires cause disastrous damage to both human life and ecosystem. Therefore, it is essential to detect forest fires in the early stage to reduce the damage. Convolutional Neural Networks (CNNs) are widely used for forest fire detection. This paper proposes a new backbone network for a CNN-based forest fire detection model. The proposed backbone network can detect the plumes of smoke well by decomposing the conventional convolution into depth-wise and coordinate ones to better extract information from objects that spread along the vertical dimension. Experimental results show that the proposed backbone network outperforms other popular ones by achieving a detection accuracy of up to 52.6 AP.1