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Research and Implementation of Forest Fire Detection Algorithm Improvement

Xi Zhou

Xi Zhou

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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Zhou, Xi
 e 
Changyuan Wang

Changyuan Wang

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
Cerca questo autore su
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Wang, Changyuan
  
16 mar 2024
Research and Implementation of Forest Fire Detection Algorithm Improvement's Cover Image
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Pubblicato online: 16 mar 2024
Pagine: 90 - 102
DOI: https://doi.org/10.2478/ijanmc-2023-0080
Parole chiave
© 2023 Xi Zhou et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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 Environment Detail
programming language Python3.8.5
operating system Windows 10
deep learning framework Pytorch 1.8.0
GPU 4x NVIDIA TITIAN V

DATASET SETTINGS

Dataset Training Test Validation Total
Homemade forest fire data set 1442 617 617 2676
Other institutes data set 600 200 200 1000

TRAINING SETTINGS PARAMETERS

Training parameters Detail
Epochs 100
Batch-size 16
Image-size 6 40 × 640
Initial learning rate 0.01
Optimization algorithm SGD

COMPARATIVE TEST RESULTS OF THE MODEL

Model P R FPS
YOLOv5s 0.811 0.786 59
YOLOv5s + CBAM 0.814 0.790 60
YOLOv5s + SE 0.810 0.787 5 8
YOLOv5s +ECA 0.812 0.791 5 9
YOLOv5s + dsCBAM 0.812 0.787 62
YOLOv5s + dsCBAM +Alpha-IoU 0.821 0.813 61
YOLOv5s + dsCBAM + SIoU 0.860 0.834 60
YOLOv5s + dsCBAM+ VariFocal (Ours) 0.871 0.816 64
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