Real-time forest smoke detection using hand-designed features and deep learning

In this paper, a fast and accurate video smoke detection algorithm is proposed to extract a powerful feature representation of forest fire. Because of the irregular shape, the variation of colors and hard-to-depict texture of smoke, it is incredibly challenging to detect smoke quickly and accurately in a complex environment. We proposes a smoke detection algorithm that combines manual features and deep learning features, an approach that differs from those of existing detection methods. First, a manual design algorithm is used to extract the suspected smoke area, which is then employed as input to the improved very small deep neural network SqueezeNet model to achieve smoke detection. Extensive detection experiments are conducted, and the results showed that the proposed method is able to differentiate smoke from other challenging objects. Compared with the existing forest fire algorithms, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower requirements for hardware devices, and these features are worthy of promotion.