Detection of Wildfires along Transmission Lines Using Deep Time and Space Features
- Autores: Yuan J.1, Wang L.2, Wu P.1, Gao C.3, Sun L.1
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Afiliações:
- Jiangsu Electric Power Information Technology Co., Ltd.
- Qianjiang College
- State Grid Jiangsu Electric Power Company
- Edição: Volume 28, Nº 4 (2018)
- Páginas: 805-812
- Seção: Applied Problems
- URL: https://journals.rcsi.science/1054-6618/article/view/195510
- DOI: https://doi.org/10.1134/S1054661818040168
- ID: 195510
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Resumo
Traditional wildfire detection methods are of low efficiency and cannot meet user needs, a novel method based on deep time and space features along transmission line is proposed in this paper, which uses ViBe algorithm to detect movements in videos, and extracts static deep feature in the space domain and dynamic optical flow feature in the time domain respectively. At last the deep convolutional neural network model in cascade is used to classify and find out real wildfire regions. By using combined deep features extracted from dynamic time-domain and static space-domain respectively, our method can eliminate the interference of movements of other objects with similar colors.
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Sobre autores
Jie Yuan
Jiangsu Electric Power Information Technology Co., Ltd.
Autor responsável pela correspondência
Email: java_mc@163.com
República Popular da China, Nanjing, 210000
Lidong Wang
Qianjiang College
Email: java_mc@163.com
República Popular da China, Hangzhou, Zhejiang, 310036
Peng Wu
Jiangsu Electric Power Information Technology Co., Ltd.
Email: java_mc@163.com
República Popular da China, Nanjing, 210000
Chao Gao
State Grid Jiangsu Electric Power Company
Email: java_mc@163.com
República Popular da China, Nanjing, 210024
Lingqing Sun
Jiangsu Electric Power Information Technology Co., Ltd.
Email: java_mc@163.com
República Popular da China, Nanjing, 210000
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