Vision-based fire detection facilities work better under new deep learning model

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Fast and accurate fire detection is significant to the sustainable development of human society and Earth ecology. The existence of objects with similar characteristics to fire increases the difficulty of vision-based fire detection. Improving the accuracy of fire detection by digging deeper visual features of fire always remains challenging.

Recently, researchers from the Institute of Acoustics of the Chinese Academy of Sciences (IACAS) have proposed an efficient deep learning model for fast and accurate vision-based fire detection. The model is based on multiscale feature extraction, implicit deep supervision, and channel attention mechanism.
The researchers utilized the real-time acquired image as the input of the model and normalized the image.
At the low-level feature extraction stage, they introduced the multiscale feature extraction mechanism to enrich spatial detail information, which enhanced the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism was employed to enhance the interaction among information flows.
Finally, the researchers used the channel attention mechanism to selectively emphasize the features contributing to the task, and effectively suppressed the interference of image noise.
The experimental results demonstrated that the accuracy of this efficient deep learning model for fire detection achieved 95.3%, but the model size …

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