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Journal Article

Citation

Zhang Q, Lin G, Zhang Y, Xu G, Wang J. Procedia Eng. 2018; 211: 441-446.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.proeng.2017.12.034

PMID

unavailable

Abstract

In this paper, Faster R-CNN was used to detect wildland forest fire smoke to avoid the complex manually feature extraction process in traditional video smoke detection methods. Synthetic smoke images are produced by inserting real smoke or simulative smoke into forest background to solve the lack of training data. The models trained by the two kinds of synthetic images respectively are tested in dataset consisting of real fire smoke images. The results show that simulative smoke is the better choice and the model is insensitive to thin smoke. It may be possible to further boost the performance by improving the synthetic process of forest fire smoke images or extending this solution to video sequences.


Language: en

Keywords

deep learning; faster R-CNN; synthetic smoke image; video smoke detection; wildland forest fire smoke

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