基于双通道深度CNN的烟雾浓度测量方法

doi:10.16451/j.cnki.issn1003-6059.202109007

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Abstract In the existing methods for measuring smoke density based on video images, features are mainly extracted manually, and the external conditions, such as ambient light and background, are required as known conditions. To improve the directness and practicability of the smoke density measurement method, the corresponding relationship between smoke images and their density values is established through the Aerosolization equation. The smoke density dataset is established as well. A smoke density measurement method based on dual-channel deep convolution neural network(DCCNN) is proposed to realize the end-to-end direct measurement of smoke density. In DCCNN, 1×1 convolution is used for channel data fusion, and jump connection is introduced to make the neural network training faster. The self-attention mechanism is also introduced to learn the importance of hidden features automatically. Finally, the features extracted from two channels are combined to obtain comprehensive measurement results. Experiments show that DCCNN gains lower mean absolute error and higher comprehensive performance.

Key words： Smoke Density Deep Convolutional Neural Network Deep Learning Aerosolization Equation Attention Mechanism

Received: 30 April 2021

ZTFLH:

TP 391.4

Fund:National Natural Science Foundation of China(No.61872166), Six Talent Peaks Project in Jiangsu Province(No.XYDXX-161)

Corresponding Authors: XIE Zhenping, Ph.D., professor. His research interests include knowledge modeling and cognitive lear-ning.

About author:: MO Hongfei, master student. His research interests include computer vision.

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Cite this article:

MO Hongfei,XIE Zhenping. Smoke Density Measurement Method Based on Dual-Channel Deep CNN[J]. , 2021, 34(9): 844-852.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202109007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I9/844

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