Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Lin Gaohua"'
Video smoke detection is a promising fire detection method especially in open or large spaces and outdoor environments. Traditional video smoke detection methods usually consist of candidate region extraction and classification, but lack powerful cha
Externí odkaz:
http://arxiv.org/abs/1809.02802
This paper proposes a method for video smoke detection using synthetic smoke samples. The virtual data can automatically offer precise and rich annotated samples. However, the learning of smoke representations will be hurt by the appearance gap betwe
Externí odkaz:
http://arxiv.org/abs/1709.08142
Publikováno v:
Fire Safety Journal 93C (2017) pp. 53-59
In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically p
Externí odkaz:
http://arxiv.org/abs/1703.10729
Publikováno v:
In Fire Safety Journal April 2019 105:277-285
Autor:
Dai, Peiwen, Zhang, Qixing, Lin, Gaohua, Shafique, Muhammad Masoom, Huo, Yinuo, Tu, Ran, Zhang, Yongming
Publikováno v:
Frontiers in Energy Research. 10
The widespread use of renewable energy resources requires more immediate and effective fire alarms as a preventive measure. The fire is usually weak in the initial stages, which is not conducive to detection and identification. This paper validates a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4f99a30fe1ed9192451642f065015a8
https://doi.org/10.3389/fenrg.2022.848754
https://doi.org/10.3389/fenrg.2022.848754
Akademický článek
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Publikováno v:
Fire Technology. 55:1827-1847
Research on video smoke detection has become a hot topic in fire disaster prevention and control as it can realize early detection. Conventional methods use handcrafted features rely on prior knowledge to recognize whether a frame contains smoke. Suc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30c995659fe1f667ccf7f74558926482
https://doi.org/10.1007/s10694-019-00832-w
https://doi.org/10.1007/s10694-019-00832-w
Publikováno v:
IEEE Access, Vol 7, Pp 29471-29483 (2019)
Video smoke detection is a promising method for early fire prevention. However, it is still a challenging task for application of video smoke detection in real-world detection systems, as the limitations of smoke image samples for training and lack o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acb6afe54a38f88f5ebe8e95ed2b1f43
https://ieeexplore.ieee.org/document/8657935/
https://ieeexplore.ieee.org/document/8657935/
Publikováno v:
Procedia Engineering. 211:441-446
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2978b314ea7d7f89ca432f7ca797800
https://doi.org/10.1016/j.proeng.2017.12.034
https://doi.org/10.1016/j.proeng.2017.12.034
Publikováno v:
Fire Safety Journal. 93:53-59
In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72c37c10bc029c849d054e5c8236f756
https://doi.org/10.1016/j.firesaf.2017.08.004
https://doi.org/10.1016/j.firesaf.2017.08.004