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Real-Time Video Fire Detection via Modified YOLOv5 Network Model

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Abstract

Accidental fire outbreak threatens people's life and property safety, and it is of great significance to study fire detection and alarm early. The detection range of traditional fire detectors is limited, and the conventional detection algorithm has the problems of low precision and long detection time. Aiming at these problems, a video fire detection method based on improved YOLOv5 is proposed in this paper. To improve the ability of feature extraction and small-scale target detection, the dilated convolution module is introduced into the SPP module of YOLOv5, the activation function GELU and the prediction bounding box suppression DIoU-NMS are employed in the structure of the improved YOLOv5. The experimental results show that the algorithm has fast detection speed and high detection accuracy. It can accurately detect not only large-scale flame but also small-scale flame in the early stage of fire. The precision and recall of the improved small YOLOv5 are 0.983 and 0.992, the mAP@.5 is as high as 0.993, and the detection speed reaches 125 FPS. The proposed method can well suppress false detection and missed detection in complex lighting environments and improve the robustness and reliability of fire detection, meet the performance requirements of the video fire detection task.

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Acknowledgements

Thanks the National Natural Science Foundation of China, Natural Science Foundation of Tibet Autonomous Region, and Scientific Research Fund Project of Xianyang Normal University support for this work.

Funding

National Natural Science Foundation of China (62073218); Natural Science Foundation of Tibet Autonomous Region (XZ202001ZR0048G); Scientific Research Project of Xianyang Science and Technology Bureau (2020K02-14); Scientific Research Fund Project of Xianyang Normal University ( XSYK20025).

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Authors and Affiliations

  1. School of Computer Science, Xianyang Normal University, Xianyang, 712000, Shaanxi, China

    Zongsheng Wu & Hong Li

  2. School of Information Engineering, Xizang Minzu University, Xianyang, 712082, Shaanxi, China

    Ru Xue

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  1. Zongsheng Wu
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  2. Ru Xue
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  3. Hong Li
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Correspondence to Zongsheng Wu.

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Wu, Z., Xue, R. & Li, H. Real-Time Video Fire Detection via Modified YOLOv5 Network Model. Fire Technol 58, 2377–2403 (2022). https://doi.org/10.1007/s10694-022-01260-z

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