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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1486212

YOLOGX: An Improved Forest Fire Detection Algorithm Based on YOLOv8

Provisionally accepted
Caixiong Li Caixiong Li 1Yue Du Yue Du 1*Xing Zhang Xing Zhang 2*Peng Wu Peng Wu 2*
  • 1 School of Computer and Information Science, Qinghai Institute of Technology, Xining,, China
  • 2 School of Computer and Information Science, Qinghai Institute of Technology, Xining, China

The final, formatted version of the article will be published soon.

    To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a highprecision algorithm, YOLOGX. YOLOGX integrates three pivotal technologies: First, the GD mechanism fuses and extracts features from multi-scale information, significantly enhancing the detection capability for fire targets of varying sizes. Second, the SE-ResNeXt module is integrated into the detection head, optimizing feature extraction capability, reducing the number of parameters, and improving detection accuracy and efficiency. Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process.YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. These enhancements establish YOLOGX as a robust and efficient solution for forest fire detection, providing significant improvements in accuracy and reliability.

    Keywords: Forest Fire Detection, YOLOv8, GD mechanism, SE-ResNeXt module, Focal-SIoU loss function

    Received: 17 Sep 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Li, Du, Zhang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yue Du, School of Computer and Information Science, Qinghai Institute of Technology, Xining,, China
    Xing Zhang, School of Computer and Information Science, Qinghai Institute of Technology, Xining, China
    Peng Wu, School of Computer and Information Science, Qinghai Institute of Technology, Xining, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.