An improved lightweight fire detection algorithm based on cascade sparse query

Zhang Xiaoxue; Wang Yu; Wu Siyuan; Sun Bangyong

doi:10.12086/oee.2023.230216

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Zhang X X, Wang Y, Wu S Y, et al. An improved lightweight fire detection algorithm based on cascade sparse query[J]. Opto-Electron Eng, 2023, 50(10): 230216. doi: 10.12086/oee.2023.230216

Citation:

Zhang X X, Wang Y, Wu S Y, et al. An improved lightweight fire detection algorithm based on cascade sparse query[J]. Opto-Electron Eng, 2023, 50(10): 230216. doi: 10.12086/oee.2023.230216

An improved lightweight fire detection algorithm based on cascade sparse query

1.
College of Printing, Packaging and Digital Media, Xi’an University of Technology, Xi’an, Shaanxi 710054, China
2.
Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, China

Fund Project: Project supported by National Natural Science Foundation of China (62076199), Key Research and Development Program of Shaanxi (2022ZDLGY01-03), Open Fund of the State Key Laboratory of Transient Optics and Photonic Technology (SKLST202214), Key Scientific Research Program of Shaanxi Provincial Department of Education (23JY063), and Scientific Research Program of Xi’an (22GXFW0088)

More Information

^*Corresponding author: sunbangyong@xaut.edu.cn

Received Date 01 September 2023

Revised Date 15 November 2023

Accepted Date 15 November 2023

Published Date 25 October 2023

Abstract

Abstract

To address the challenges of complex models, slow detection speed, and high false detection rate during fire detection, a lightweight fire detection algorithm is proposed based on cascading sparse query mechanism, called LFNet. In the study, firstly, a lightweight feature extraction module ECDNet is established to extract more fine-grained features in different levels of feature layers by embedding the lightweight attention module ECA (efficient channel attention) in YOLOv5s backbone network, which is used to solve the multi-scale of flame and smoke in fire detection. Secondly, deep feature extraction module FPN+PAN is adopted to improve multi-scale fusion of feature maps at different levels. Finally, the Cascade Sparse Query embedded lightweight cascade sparse query module is applied to improve the detection accuracy of small flames and thin smoke in early fires. Experimental results show that the comprehensive performance of the proposed method in objective indicators such as mAP and Precision is the best on SF-dataset, D-fire and FIRESENSE. Furthermore, the proposed model achieves lower parameters and higher detection accuracy, which can meet the fire detection requirements of challenge scenes.
- object detection /
- fire detection /
- lightweight /
- cascade sparse query /
- Slimming

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References

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Overview

Overview

The Visual Fire Detection task is designed to detect flames and smoke using visual algorithms from images and videos to achieve fire alarms. In recent years, fire detection algorithms based on convolutional neural networks have greatly improved the detection accuracy of flames and smoke. However, the following questions still exist in the current methods: 1) The generalization ability of the model still needs to be improved; 2) Low fire detection accuracy for small objects; 3) The tradeoff between the detection accuracy and speed fails to achieve. In order to overcome the above problems, a lightweight fire detection algorithm is proposed based on cascading sparse query mechanism, called LFNet. In this study, firstly, a lightweight feature extraction module ECDNet is established to extract more fine-grained features in different levels of feature layers by embedding the lightweight attention module ECA (Efficient Channel Attention) in YOLOv5s backbone network, which is used to solve the multi-scale of flame and smoke in fire detection. Secondly, deep feature extraction module FPN+PAN is adopted to improve multi-scale fusion of feature maps at different levels. Finally, the Cascade Sparse Query embedded lightweight cascade sparse query module is applied to enhance the detection accuracy of small flames and thin smoke in early fires. Furthermore, to further decrease the parameters and calculation of the model, the Slimming pruning algorithm is adopted to change the size of the model. The experimental results on the three fire datasets of SF-dataset, D-fire and FIRESENSE show that the comprehensive performance of the proposed method on objective indicators such as mAP and Recall is best. On the SF-dataset dataset, the LFNet achieves the best mAP and Recall, which are 71.76% and 52.98%, respectively. On the D-fire dataset, the mAP of our method reachs 71.76%, which is far superior to other fire detection methods. On the FIRESENSE dataset, our method achieves 70.61% mAP. Our method effectively alleviates the main problems of current fire detection algorithms, such as low detection accuracy, high missed detection rate for small objects, and difficulty in balancing speed and accuracy. The network trains and builds a fire detection model on self-built datasets and other fire datasets. The experimental results show that on the condition that the model size is suitable and the speed is relatively fast, our method achieves an optimal detection effect on both the self-built fire dataset and the public fire datasets, and will potentially promote the application of deep learning-based fire detection methods in industries.