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Research and Implementation of Forest Fire Detection Algorithm Improvement Cover

Research and Implementation of Forest Fire Detection Algorithm Improvement

By: Xi Zhou and  Changyuan Wang  
Open Access
|Mar 2024

Figures & Tables

Figure 1.

Part of the image data used for training: (a) Fire with poor resolution. (b) Fire in a small area. (c) Fire with flame obstruction. (d) Fire disturbed by smoke.
Part of the image data used for training: (a) Fire with poor resolution. (b) Fire in a small area. (c) Fire with flame obstruction. (d) Fire disturbed by smoke.

Figure 2.

YOLOv 5 input data enhancement method
YOLOv 5 input data enhancement method

Figure 3.

CBAM overall structure
CBAM overall structure

Figure 4.

CAM structure
CAM structure

Figure 5.

SAM structure
SAM structure

Figure 6.

Ordinary convolution
Ordinary convolution

Figure 7.

Depth separation convolution: (a) Depth convolution. (b) Pointwise convolution
Depth separation convolution: (a) Depth convolution. (b) Pointwise convolution

Figure 8.

Improved model framework
Improved model framework

Figure 9.

The position of CBAM in YOLOv5s 6.0 version
The position of CBAM in YOLOv5s 6.0 version

Figure 10.

Experimental identification results: (a) Improved model. (b) Original model.
Experimental identification results: (a) Improved model. (b) Original model.

Figure 11.

Experimental Experimental results of misdetection of forest street lights at night. (a) Original model. (b) Improved model.
Experimental Experimental results of misdetection of forest street lights at night. (a) Original model. (b) Improved model.

EXPERIMENTAL SETTINGS

Lab EnvironmentDetail
programming languagePython3.8.5
operating systemWindows 10
deep learning frameworkPytorch 1.8.0
GPU4x NVIDIA TITIAN V

DATASET SETTINGS

DatasetTrainingTestValidationTotal
Homemade forest fire data set14426176172676
Other institutes data set6002002001000

TRAINING SETTINGS PARAMETERS

Training parametersDetail
Epochs100
Batch-size16
Image-size6 40 × 640
Initial learning rate0.01
Optimization algorithmSGD

COMPARATIVE TEST RESULTS OF THE MODEL

ModelPRFPS
YOLOv5s0.8110.78659
YOLOv5s + CBAM0.8140.79060
YOLOv5s + SE0.8100.7875 8
YOLOv5s +ECA0.8120.7915 9
YOLOv5s + dsCBAM0.8120.78762
YOLOv5s + dsCBAM +Alpha-IoU0.8210.81361
YOLOv5s + dsCBAM + SIoU0.8600.83460
YOLOv5s + dsCBAM+ VariFocal (Ours)0.8710.81664
Language: English
Page range: 90 - 102
Published on: Mar 16, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Xi Zhou, Changyuan Wang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.