基于稠密卷积神经网络的烟雾识别方法

doi:10.11772/j.issn.1001-9081.2019091583

计算机应用 ›› 2020, Vol. 40 ›› Issue (5): 1465-1469.DOI: 10.11772/j.issn.1001-9081.2019091583

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于稠密卷积神经网络的烟雾识别方法

程广涛¹, 巩家昌², 李建¹

1.国家消防工程技术研究中心研发部，天津 300381
2.中国刑事警察学院声像资料检测技术系，沈阳 110854

收稿日期:2019-09-17 修回日期:2019-10-25 出版日期:2020-05-10 发布日期:2020-05-15
通讯作者: 程广涛(1983—)
作者简介:程广涛(1983—)，男，黑龙江大庆人，工程师，博士，主要研究方向：图像处理、模式识别、深度学习；巩家昌(1983—)，男，山东临沂人，讲师，博士，主要研究方向：图像处理、模式识别；李建(1992—)，女，河北沧州人，工程师，硕士，主要研究方向：数据挖掘、图像处理。
基金资助:
应急管理部天津消防研究所基金资助项目(2018SJ20)。

Smoke recognition method based on dense convolutional neural network

CHENG Guangtao¹, GONG Jiachang², LI Jian¹

1.Department of Research and Development, National Center for Fire Engineering Technology, Tianjin 300381, China
2.Department of Audio-Visual Information Detection Technology, Criminal Investigation Police University of China,ShenyangLiaoning 110854, China

Received:2019-09-17 Revised:2019-10-25 Online:2020-05-10 Published:2020-05-15
Contact: CHENG Guangtao, born in 1983, Ph. D., engineer. His research interests include image processing, pattern recognition, deep learning.
About author:CHENG Guangtao, born in 1983, Ph. D., engineer. His research interests include image processing, pattern recognition, deep learning.GONG Jiachang, born in 1983, Ph. D., lecturer. His research interests include image processing, pattern recognition.LI Jian, born in 1992, M. S., engineer. Her research interests include data mining, image processing.
Supported by:
This work is partially supported by the Program of Tianjin Fire Research Institute of Ministry of Emergency Management (2018SJ20).

摘要/Abstract

摘要：

针对传统烟雾检测方法中提取的图像特征鲁棒性较差的问题，提出了基于稠密卷积神经网络（DenseNet）的烟雾识别方法。首先，利用卷积操作和特征图融合构建稠密网络块，在卷积层之间设计稠密连接机制，以增强稠密网络块结构内的信息流通和特征重利用；然后，将已构建的稠密网络块叠加成稠密卷积神经网络用于烟雾识别，节省计算资源的同时提升对烟雾图像特征的表达能力；最后，针对烟雾图像数据量较小的问题，采取数据增强技术进一步改善训练模型的识别能力。在公开烟雾数据集上对提出的方法进行实验验证，实验结果表明，所提方法的模型大小只有0.44 MB，在两个测试集上的准确率分别为96.20%和96.81%。

关键词: 烟雾识别, 稠密连接, 卷积神经网络, 深度学习, 数据增强

Abstract:

To address the poor robustness of the extracted image features in traditional smoke detection methods, a smoke recognition method based on Dense convolution neural Network (DenseNet) was proposed. Firstly, the dense network blocks were constructed by applying convolution operation and feature map fusion, and the dense connection mechanism was designed between the convolution layers, so as to promote the information circulation and feature reuse in the dense network block structure. Secondly, the DenseNet was designed by stacking the designed dense network blocks for smoke recognition, saving the computing resources and enhancing the expression ability of smoke image features. Finally, aiming at the problem of small smoke image data size, data augmentation technology was adopted to further improve the recognition ability of the training model. Experiments were carried out on public smoke datasets. The experimental results illustrate that the proposed method achieves high accuracy of 96.20% and 96.81% on two test sets respectively with only 0.44 MB model size.

Key words: smoke recognition, dense connection, Convolutional Neural Network (CNN), deep learning, data augmentation

中图分类号:

TP391.41

程广涛, 巩家昌, 李建. 基于稠密卷积神经网络的烟雾识别方法[J]. 计算机应用, 2020, 40(5): 1465-1469.

CHENG Guangtao, GONG Jiachang, LI Jian. Smoke recognition method based on dense convolutional neural network[J]. Journal of Computer Applications, 2020, 40(5): 1465-1469.

参考文献

1 史劲亭,袁非牛,夏雪 .视频烟雾检测研究进展[J]. 中国图象图形学报, 2018, 23(3): 303-322. (SHI J T, YUAN F N, XIA X. Video smoke detection: a literature survey[J]. Journal of Image and Graphics, 2018, 23(3): 303-322.)
2 许峰,于春雨,徐放 . 视频烟雾火灾探测技术研究趋势[J]. 消防科学与技术, 2012, 31(11): 1185-188. (XU F, YU C Y, XU F. Development of the video smoke detection technology research[J]. Fire Science and Technology, 2012, 31(11): 1185-1188.)
3 FERRARI R J , ZHANG H , KUBE C R . Real-time detection of steam in video images[J]. Pattern Recognition, 2007, 40(3): 1148-1159.
4 YE S , BAI Z , CHEN H , et al . An effective algorithm to detect both smoke and flame using color and wavelet analysis[J]. Pattern Recognition and Image Analysis, 2017, 27(1): 131-138.
5 YUAN F . Video-based smoke detection with histogram sequence of LBP and LBPV pyramids[J]. Fire Safety Journal, 2011, 46(3): 132-139.
6 YUAN F , SHI J , XIA X , et al . High-order local ternary patterns with locality preserving projection for smoke detection and image classification[J]. Information Sciences, 2016, 372: 225-240.
7 KRIZHEVSKY A , SUTSKEVER I , HINTO G E . ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems. New York: Curran Associates, 2012: 1097-1105.
8 LIU L , OUYANG W , WANG X , et al . Deep learning for generic object detection: a survey[EB/OL]. [2019-08-22]. https://arxiv.org/pdf/1809.02165.pdf.
9 ZOU Z , SHI Z , GUO Y , et al . Object detection in 20 years: a survey[EB/OL]. [2019-05-16]. https://arxiv.org/pdf/1905.05055.pdf.
10 方国康,李俊,王垚儒 . 基于深度学习的ARM平台实时人脸识别[J]. 计算机应用, 2019, 39(8): 2217-2222. (FANG G K, LI J, WANG Y R. Real-time face recognition on ARM platform based on deep learning[J]. Journal of Computer Applications, 2019, 39(8):2217-2222.)
11 YIN Z , WAN B , YUAN F , et al . A deep normalization and convolutional neural network for image smoke detection[J]. IEEE Access, 2017, 5: 18429-18438.
12 王文朋,毛文涛,何建樑,等 . 基于深度迁移学习的烟雾识别方法[J]. 计算机应用, 2017, 37(11): 3176-3181, 3193. WANG W P , MAO W T , HE J L , et al . Smoke recognition based on deep transfer learning[J]. Journal of Computer Applications, 2017, 37(11): 3176-3181, 3193.
13 SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[EB/OL]. [2018-03-22]. https://arxiv.org/pdf/1409.1556.pdf.
14 YUAN F , ZHANG L , WAN B , et al . Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition[J]. Machine Vision and Applications, 2019, 30(2): 345-358.
15 HUANG G , LIU Z , MAATEN L VAN DER , et al . Densely connected convolutional networks[EB/OL]. [2016-08-24]. https://arxiv.org/pdf/1608.06993.pdf.

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基于稠密卷积神经网络的烟雾识别方法

Smoke recognition method based on dense convolutional neural network

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