Fire detection method based on lightweight YOLOv4

Huilin Wang; Xiaoguang Xu; Xuyao Hao; Mengnan Li; Qi Wang

doi:10.1117/12.2661201

9 December 2022 Fire detection method based on lightweight YOLOv4

Huilin Wang, Xiaoguang Xu, Xuyao Hao, Mengnan Li, Qi Wang

Proceedings Volume 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022); 124920H (2022) https://doi.org/10.1117/12.2661201
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 2022, Wuhan, China

Abstract

A fire detection model with improved YOLOv4 is proposed for mobile devices with limited computing resources and less accurate object localization. Firstly, the network model of YOLOv4 object detection algorithm is modified, and a depthwise separable convolution network is used instead of traditional convolution in the feature extraction network part to realize the lightweight of fire detection model. Then the Loss function is optimized to solve the problem of inaccurate object detection frame localization. The experimental results show that compared with YOLOv4, the improved algorithm reduces the model parameter by 60.7 % and the detection speed increases by 27.9 %. It is more favorable for the model to be equipped on mobile devices.

Citation Download Citation

Huilin Wang, Xiaoguang Xu, Xuyao Hao, Mengnan Li, and Qi Wang "Fire detection method based on lightweight YOLOv4", Proc. SPIE 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 124920H (9 December 2022); https://doi.org/10.1117/12.2661201

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KEYWORDS

Convolution

Flame detectors

Detection and tracking algorithms

RGB color model

Feature extraction

Instrument modeling

Mobile devices

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