Algorithm for segmentation of smoke using the improved DeeplabV3 network

被引：0
|
作者：
Wang Z. [1 ]
Su Y. [1 ]
Liu Y. [2 ]
Zhang W. [3 ]
机构：
[1] School of Electrical Automation and Information Engineering, Tianjin University, Tianjin
[2] School of Electronic Information and Optical Engineering, Nankai University, Tianjin
[3] School of Microelectronics, Tianjin University, Tianjin
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2019年 / 46卷 / 06期
关键词：
Attention mechanism; Deep learning; Deformable convolution; Image processing; Semantic segmentation; Smoke detection;
D O I：
10.19665/j.issn1001-2400.2019.06.008
中图分类号：
学科分类号：
摘要：
Existing smoke detection methods depend mostly on features which are selected manually and the smoke areas in video images often cannot be segmented accurately. This paper proposes an improved DeeplabV3 smoke segmentation algorithm based on this. A feature refinement module is added after the basic encoder network to weaken the gridding effects caused by dilated convolutions. For the non-rigid objects such as smoke with variable scales and postures, the Atrous Spatial Pyramid Pooling module is combined with the deformable convolution to better adapt to the smoke deformation. And a channel attention decoder module is proposed to further restore the spatial details of smoke images. According to the test of the smoke image data set, the proposed model has a faster average prediction speed of 71.73ms per image. Besides, compared with the DeeplabV3 network, this algorithm can lead to a higher MPA (Mean Pixel Accuracy) of 97.78% and a higher MIoU (Mean Intersection over Union) of 91.21%, thus improving the performance by 0.56% and 2.17% respectively, and making it more suitable for smoke segmentation. Public smoke video test results show that this model outperforms other video-based smoke detection methods for the detection rate, and that it is of certain research significance and practical value. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：52 / 59
页数：7
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