<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To overcome low efficiency and accuracy of existing forest fire detection algorithms, this paper proposes a network model to enhance the real-time and robustness of detection. This structure is based on the YOLOv5 target detection algorithm and combines the backbone network with The feature extraction module combines the attention module dsCBAM improved by depth-separable convolution, and replaces the loss function CIoU of the original model with a VariFocal loss function that is more suitable for the imbalanced characteristics of positive and negative samples in the forest fire data set. Experiments were conducted on a self-made and public forest fire data set. The accuracy and recall rate of the model can reach 87.1% and 81.6%, which are 7.40% and 3.20% higher than the original model, and the number of images processed per second reaches 64 frames, a growth rate of 8.47%. At the same time, this model was compared horizontally with other improved methods. The accuracy, recall rate and processing speed were all improved in the range of 3% to 10%. The effectiveness of the improved method in this article was verified, and the external perception level of the forest fire scene was deeper.</p>
</abstract>

To overcome low efficiency and accuracy of existing forest fire detection algorithms, this paper proposes a network model to enhance the real-time and robustness of detection. This structure is based on the YOLOv5 target detection algorithm and combines the backbone network with The feature extraction module combines the attention module dsCBAM improved by depth-separable convolution, and replaces the loss function CIoU of the original model with a VariFocal loss function that is more suitable for the imbalanced characteristics of positive and negative samples in the forest fire data set. Experiments were conducted on a self-made and public forest fire data set. The accuracy and recall rate of the model can reach 87.1% and 81.6%, which are 7.40% and 3.20% higher than the original model, and the number of images processed per second reaches 64 frames, a growth rate of 8.47%. At the same time, this model was compared horizontally with other improved methods. The accuracy, recall rate and processing speed were all improved in the range of 3% to 10%. The effectiveness of the improved method in this article was verified, and the external perception level of the forest fire scene was deeper.

Research and Implementation of Forest Fire Detection Algorithm Improvement

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research and Implementation of Forest Fire Detection Algorithm Improvement"}

To overcome low efficiency and accuracy of existing forest fire detection algorithms, this paper proposes a network model to enhance the real-time and...

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

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

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

Research and Implementation of Forest Fire Detection Algorithm Improvement

Publié en ligne: 16 mars 2024

Pages: 90 - 102

DOI: https://doi.org/10.2478/ijanmc-2023-0080

Mots clés
Fire Target Detection, YOLOv5, CBAM, Depth Separable Convolution, VariFocal Loss

© 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.

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EXPERIMENTAL SETTINGS

DATASET SETTINGS

TRAINING SETTINGS PARAMETERS

COMPARATIVE TEST RESULTS OF THE MODEL

Research and Implementation of Forest Fire Detection Algorithm Improvement

Publié en ligne: 16 mars 2024

Pages: 90 - 102

DOI: https://doi.org/10.2478/ijanmc-2023-0080

Mots clésFire Target Detection, YOLOv5, CBAM, Depth Separable Convolution, VariFocal Loss

© 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.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

EXPERIMENTAL SETTINGS

DATASET SETTINGS

TRAINING SETTINGS PARAMETERS

COMPARATIVE TEST RESULTS OF THE MODEL

Mots clés
Fire Target Detection, YOLOv5, CBAM, Depth Separable Convolution, VariFocal Loss