Home > Published Issues > 2023 > Volume 11, No. 3, September 2023 >
JOIG 2023 Vol.11(3): 227-232
doi: 10.18178/joig.11.3.227-232

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

Deepamoni Mahanta, Deepika Hazarika, and Vijay Kumar Nath*
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea;
Email: hqquyen@gmail.com (Q.Q.H.), quylam925@gmail.com (Q.L.H.)
*Correspondence: hoonoh@ulsan.ac.kr (H.O.)

Manuscript received April 6, 2023; revised May 20, 2023; accepted June 4, 2023.

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

Keywords—convolutional neural network, object detection, forest fire detection, backbone network, depth-wise convolution

Cite: Quy Quyen Hoang, Quy Lam Hoang, and Hoon Oh, "An Efficient Backbone for Early Forest Fire Detection Based on Convolutional Neural Networks," Journal of Image and Graphics, Vol. 11, No. 3, pp. 227-232, September 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.