LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework
Document Type: Journal Article
Author(s): Jianwei Li; Huan Tang; Xingdong Li; Hongqiang Dou; Ru Li
Publication Year: 2024

Cataloging Information

  • convolutional neural network
  • deep learning
  • extreme wildfires
  • fire detection
  • fire safety
  • multi-level feature fusion
  • object detection
  • YOLOv5
Record Maintained By:
Record Last Modified: February 11, 2024
FRAMES Record Number: 69075


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.

Online Link(s):
Li, Jianwei; Tang, Huan; Li, Xingdong; Dou, Hongqiang; Li, Ru. 2024. LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework. International Journal of Wildland Fire 33:WF23044.