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A visualized fire detection method based on convolutional neural network beyond anchor

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Abstract

Convolutional neural networks (CNNs) have achieved the most advanced performance in visual object detection and semantic segmentation tasks, so that the application of CNNs in fire detection will greatly improve the accuracy of detection. However, the existing fire detection methods are difficult to achieve real-time detection due to the complicated model, slow detection rate, low detection accuracy, and the huge amount of calculation, also, high memory usage limits the implementation of some methods on the device. Therefore, a fast and efficient fire detection model, whose design is derived from the lightweight network MobileNetV3 and the anchor free structure, is proposed in this paper. The proposed method has achieved an accuracy of 90.2% on a self-built dataset, while the model size is only 21.4M, and the running speed can reach 29.5f/s. At the same time, comparative experiments were conducted on two public fire datasets, and according to the results, the proposed method showed better performance and faster speed, confirming that the proposed method can satisfy real-time fire detection and is achievable for application on embeddable equipment.

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Authors and Affiliations

  1. School of Microelectronics, Tianjin University, Tianjin, 300072, China

    Yuming Li, Wei Zhang & Yao Jin

  2. Collage of Electronic information and Optical Engineering, Nankai University, Tianjin, 300072, China

    Yanyan Liu

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  1. Yuming Li
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  2. Wei Zhang
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Correspondence to Yanyan Liu.

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Li, Y., Zhang, W., Liu, Y. et al. A visualized fire detection method based on convolutional neural network beyond anchor. Appl Intell 52, 13280–13295 (2022). https://doi.org/10.1007/s10489-022-03243-7

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