Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images
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...
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| Veröffentlicht in: | Procedia engineering Jg. 211; S. 441 - 446 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
2018
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| ISSN: | 1877-7058, 1877-7058 |
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| 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. |
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| AbstractList | 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. |
| Author | Zhang, Yong-ming Wang, Jin-jun Zhang, Qi-xing Lin, Gao-hua Xu, Gao |
| Author_xml | – sequence: 1 givenname: Qi-xing surname: Zhang fullname: Zhang, Qi-xing – sequence: 2 givenname: Gao-hua surname: Lin fullname: Lin, Gao-hua email: lingh@mail.ustc.edu.cn – sequence: 3 givenname: Yong-ming surname: Zhang fullname: Zhang, Yong-ming – sequence: 4 givenname: Gao surname: Xu fullname: Xu, Gao – sequence: 5 givenname: Jin-jun surname: Wang fullname: Wang, Jin-jun |
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| Cites_doi | 10.1109/IECON.2016.7793196 10.1109/CIMSA.2011.6059930 10.1007/s10694-009-0110-z 10.2991/ifmeita-16.2016.105 10.1007/s10694-014-0453-y 10.1016/j.firesaf.2017.08.004 10.1016/j.firesaf.2011.01.001 10.1109/TPAMI.2016.2577031 |
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| Keywords | faster R-CNN deep learning synthetic smoke image wildland forest fire smoke video smoke detection |
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| SubjectTerms | deep learning faster R-CNN synthetic smoke image video smoke detection wildland forest fire smoke |
| Title | Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images |
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