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A real-time fire detection method from video for electric vehicle-charging stations based on improved YOLOX-tiny

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Abstract

For the current mainstream detection methods are difficult to achieve fire detection in outdoor electric vehicle-charging station, this paper proposes a real-time fire detection method from video for electric vehicle charging stations based on improved YOLOX-tiny. CBAM attention mechanism is introduced to concatenate the spatial and channel attention information, to preserve the salient features of different shapes of flames. Depthwise Separable Convolution is used to replace Conventional Convolution to reduce the number of Parameters and FLOPs of the network, and improves the speed of detection and the deployment of the model on the embedded side. CIoU loss function is used to replace bounding box regression loss function of YOLOX-tiny, and the aspect ratio limit mechanism is added to improve the convergence speed of the loss function and make the prediction results more consistent with the actual target. Experiment shows that mAP value of improved YOLOX-tiny is 94.05\(\%\), Precision is 91.76\(\%\), and Recall is 83.27\(\%\) on the embedded side, with the video detection speed is 20 fps, which meets the demand for real-time detection for electric vehicle-charging stations.

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Data availability

The datasets generated during the current study are not publicly available due to the next experiment but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Key Research and Development Projects of Shandong Province (2019GGX101012), the Natural Science Foundation of Shandong Province (Grant No. ZR2022ME194) and the Independent Research and Innovation Project of Qingdao University of Science and Technology (S2022KY017).

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  1. Dexin Gao contributed to this work.

Authors and Affiliations

  1. School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Shandong, 266061, China

    Yifan Ju, Dexin Gao & Shiyu Zhang

  2. School of Information Science and Technology, Qingdao University of Science and Technology, Shandong, 266061, China

    Qing Yang

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  1. Yifan Ju
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Correspondence to Dexin Gao.

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Ju, Y., Gao, D., Zhang, S. et al. A real-time fire detection method from video for electric vehicle-charging stations based on improved YOLOX-tiny. J Real-Time Image Proc 20, 48 (2023). https://doi.org/10.1007/s11554-023-01309-4

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