标题:3D Parallel Fully Convolutional Networks for Real-time Video Wildfire Smoke Detection
作者:Li X.; Chen Z.; Wu Q.M.J.; Liu C.
作者机构:[Li, X] School of Control Science and Engineering, Shandong University, Jinan 250061, China.;[ Chen, Z] School of Control Science and Engineering, Sha 更多
来源:IEEE Transactions on Circuits and Systems for Video Technology
出版年:2018
DOI:10.1109/TCSVT.2018.2889193
关键词:3D-PFCN; Convolution; Convolutional neural networks; Feature extraction; Image color analysis; Image segmentation; natural scene; real-time; Three-dimensional displays; wildfire smoke detection
摘要:Wildfires have devastating consequences on ecological systems and human lives. Accurate and fast wildfire detection is crucial to reducing damage. The existing smoke detection algorithms using convolution neural network are mostly based on the classification of smoke images or patches, whereas the traditional smoke detection algorithms are often necessary to extract multiple features for integration. With the methods mentioned above, false positive is always an insurmountable problem in wildfire smoke detection. Moreover, there are few studies on the detection of wildfire smoke. Thus, to detect the wildfire smoke more intelligent, a 3D parallel fully convolutional network for wildfire smoke detection is proposed to segment the smoke regions in video sequences. Wildfire smoke detection is considered as a segmentation problem in this paper. There are more than 90 videos including various scene used for training and test. Experiments have demonstrated that our architecture can segment smoke regions accurately and eliminate the interference of natural scenes. Smoke targets in multiple scene can be detected accurately and quickly. IEEE
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059011903&doi=10.1109%2fTCSVT.2018.2889193&partnerID=40&md5=7e829736fc80610ea9d0943ba702e13f
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