Abstract
Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection. Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots. This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy. This determines smoke origin candidate points and region based on a multi-frame discrete confidence level. The results show that the new method provides a more complete smoke contour with no background interference, compared to the results using existing methods. Unlike video-based methods that rely on continuous frames, an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames. The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.
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Project funding: The work was supported by the National Natural Science Foundation of China (grants no. 32171797 and 31800549).
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Corresponding editor: Yu Lei.
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Lou, L., Chen, F., Cheng, P. et al. Smoke root detection from video sequences based on multi-feature fusion. J. For. Res. 33, 1841–1856 (2022). https://doi.org/10.1007/s11676-022-01461-w
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DOI: https://doi.org/10.1007/s11676-022-01461-w