Recent progress in vision-based fire detection is driven by convolutional neural networks. However, the existing methods fail to achieve a good tradeoff among accuracy, model size, and speed. In this paper, we propose an accurate fire detection method that achieves a better balance in the abovementioned aspects. Specifically, a multiscale feature extraction mechanism is employed to capture richer spatial details, which can enhance the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism is utilized to enhance the interaction among information flows through dense skip connections. Finally, a channel attention mechanism is employed to selectively emphasize the contribution between different feature maps. Experimental results demonstrate that our method achieves 95.3% accuracy, which outperforms the suboptimal method by 2.5%. Moreover, the speed and model size of our method are 3.76% faster on the GPU and 63.64% smaller than the suboptimal method, respectively.

中文翻译：

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一种基于多尺度特征提取，隐式深度监督和通道关注机制的高效火灾探测方法。

卷积神经网络推动了基于视觉的火灾探测的最新进展。但是，现有方法无法在准确性，模型大小和速度之间取得良好的折衷。在本文中，我们提出了一种精确的火灾探测方法，可以在上述方面达到更好的平衡。具体地，采用多尺度特征提取机制来捕获更丰富的空间细节，这可以增强类似火的物体的判别能力。然后，利用隐式深度监督机制来增强通过密集跳过连接的信息流之间的交互。最后，采用频道关注机制来选择性地强调不同特征图之间的贡献。实验结果表明，我们的方法达到了95.3％的准确度，胜过次优方法2.5％。此外，我们的方法的速度和模型大小分别在GPU上快3.76％，比在次优方法上小63.64％。