LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework

Jianwei Li; Huan Tang; Xingdong Li; Hongqiang Dou; Ru Li

doi:10.1071/WF23044

RESEARCH ARTICLE (Open Access)

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LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework

Jianwei Li

^A ^* , Huan Tang ^A , Xingdong Li ^B , Hongqiang Dou ^C and Ru Li ^D

+ Author Affiliations

- Author Affiliations

^A College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China.

^B College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China.

^C Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China.

^D Huadong Engineering Corporation Limited, Hangzhou, 311122, China.

^* Correspondence to: lwticq@163.com

International Journal of Wildland Fire 33, WF23044 https://doi.org/10.1071/WF23044

Submitted: 5 April 2023 Accepted: 29 November 2023 Published: 18 December 2023

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and existing models primarily detect the presence or absence of fires without focusing on distinguishing extreme wildfires and providing warnings.

Aims

To test a system for real time detection of four extreme wildfires.

Methods

We proposed a novel lightweight model, called LEF-YOLO, based on the YOLOv5 framework. To make the model lightweight, we introduce the bottleneck structure of MobileNetv3 and use depthwise separable convolution instead of conventional convolution. To improve the model’s detection accuracy, we apply a multiscale feature fusion strategy and use a Coordinate Attention and Spatial Pyramid Pooling-Fast block to enhance feature extraction.

Key results

The LEF-YOLO model outperformed the comparison model on the extreme wildfire dataset we constructed, with our model having excellent performance of 2.7 GFLOPs, 61 FPS and 87.9% mAP.

Conclusions

The detection speed and accuracy of LEF-YOLO can be utilised for the real-time detection of four extreme wildfires in forest fire scenes.

Implications

The system can facilitate fire control decision-making and foster the intersection between fire science and computer science.

Keywords: convolutional neural networks, deep learning, extreme wildfire, fire safety, lightweight, multiscale feature fusion, object detection, YOLO (LEF-YOLO).

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