Research on YOLOv5 forest fire recognition algorithm utilizing attention mechanism

Zizhen Feng

doi:10.1117/12.3033708

13 June 2024 Research on YOLOv5 forest fire recognition algorithm utilizing attention mechanism

Zizhen Feng

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318050 (2024) https://doi.org/10.1117/12.3033708
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

In recent years, various fire incidents have become increasingly prevalent, posing a severe threat to both life and property safety. The timely detection, early warning, and prevention of fire smoke are crucial for minimizing losses and ensuring sustainable societal development. Traditional forest fire recognition algorithms are significantly impacted by environmental conditions, making them unsuitable for expansive regions. The majority of image-based algorithms rely on manual feature extraction, contingent on the expertise of researchers. Deep learning, leveraging abundant input samples, automatically extracts features through multiple layers of neural nodes, resulting in a model with superior generalization capabilities. This paper introduces a forest fire detection algorithm based on convolutional neural networks by incorporating the non-parametric attention mechanism SimAM into the core network of YOLOv5. The attention mechanism assigns three-dimensional attention weights to wildfire feature maps, thereby augmenting the model's feature capture proficiency. Experimental results show that, without introducing additional parameters, compared to the original YOLOv5 algorithm, the accuracy, recall, and F1-score increased by 9.6%, 8.2%, and 6.2%, respectively, achieving precise identification of forest fires.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zizhen Feng "Research on YOLOv5 forest fire recognition algorithm utilizing attention mechanism", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318050 (13 June 2024); https://doi.org/10.1117/12.3033708

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KEYWORDS

Forest fires

Object detection

Data modeling

Detection and tracking algorithms

Neurons

Education and training

Feature extraction

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