Lightweight fire detection algorithm based on improved YOLOv8

Di Wu; Jinzheng Ning; Yuanzhi Wang

doi:10.1117/12.3052362

30 December 2024 Lightweight fire detection algorithm based on improved YOLOv8

Di Wu, Jinzheng Ning, Yuanzhi Wang

Author Affiliations +

Proceedings Volume 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024); 133940Q (2024) https://doi.org/10.1117/12.3052362
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 2024, Hohhot, China

Abstract

Aiming at the limitations of traditional fire detection algorithms in terms of accuracy and real-time detection, a lightweight fire detection algorithm based on improved YOLOv8 is proposed. The Slim-neck is used to improve the Neck network, reduce the number of parameters and computation of the model, and improve the detection performance of the model. The C2f-Star module is designed, and the Star block is introduced to replace the bottleneck structure of the C2f module in the Backbone network, to better capture the information of the image, and further reduce the complexity of the model. The Focaler WIoU boundary loss function is used instead of the original loss function, which reduces the influence of low-quality samples and increases the regressivity of the network bounding box. The experimental results show that the number of parameters and the computational volume of the improved model are reduced by 14.0% and 16.0%, while the precision and the mean average precision are improved by 3.0% and 1.2%, compared with the original model, which can help real-time monitoring and early warning of fire.

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

Citation Download Citation

Di Wu, Jinzheng Ning, and Yuanzhi Wang "Lightweight fire detection algorithm based on improved YOLOv8", Proc. SPIE 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 133940Q (30 December 2024); https://doi.org/10.1117/12.3052362

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KEYWORDS

Fire

Detection and tracking algorithms

Performance modeling

Education and training

Target detection

Feature extraction

Convolution

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