Fire detection based on MobileNetv3-YOLOv4

Zhuolun Xie

doi:10.1117/12.3035115

19 July 2024 Fire detection based on MobileNetv3-YOLOv4

Zhuolun Xie

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132Z (2024) https://doi.org/10.1117/12.3035115
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

To achieve accurate and rapid fire detection, this paper improves the network structure of YOLOv4 through various means. Given the numerous parameters of the original YOLOv4 network model, we introduce the lightweight MobileNetv3 backbone network to replace the original backbone, thereby reducing network complexity. Additionally, we enhance detection accuracy by incorporating the Inverted-bneck-shortcut structure in the prediction network to facilitate multi-scale feature fusion and prediction. Since publicly available fire image datasets are insufficient for training deep neural networks, we collect fire images through various methods and further expand the dataset using data augmentation techniques, ultimately creating our own fire image dataset. Experimental results demonstrate that the improved YOLOv4 fire detection model presented in this paper is approximately 1/3 the size of the original YOLOv4 model, with a nearly 40% increase in inference speed and a 6% improvement in algorithm accuracy. This suggests that the proposed MobileNetv3-YOLOv4-based fire detection algorithm can accurately and rapidly identify and locate fire targets in images.

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

Citation Download Citation

Zhuolun Xie "Fire detection based on MobileNetv3-YOLOv4", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132Z (19 July 2024); https://doi.org/10.1117/12.3035115

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KEYWORDS

Fire

Convolution

Data modeling

Education and training

Forest fires

Object detection

Performance modeling

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