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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  4/2018 - 15
View TOC | « Previous Article | Next Article »

An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data

NAMOZOV, A. See more information about NAMOZOV, A. on SCOPUS See more information about NAMOZOV, A. on IEEExplore See more information about NAMOZOV, A. on Web of Science, CHO, Y. I. See more information about CHO, Y. I. on SCOPUS See more information about CHO, Y. I. on SCOPUS See more information about CHO, Y. I. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (2,027 KB) | Citation | Downloads: 968 | Views: 854

Author keywords
smoke detectors, neural networks, image classification, image recognition, image generation

References keywords
networks(11), image(10), neural(9), deep(9), detection(8), convolutional(8), processing(7), vision(6), smoke(6), recognition(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 121 - 128
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04015
Web of Science Accession Number: 000451843400015
SCOPUS ID: 85058789954

Abstract
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Detecting smoke and fire from visual scenes is a demanding task, due to the high variance of the color and texture. A number of smoke and fire image classification approaches have been proposed to overcome this problem; however, most of them rely on either rule-based methods or on handcrafted features. We propose a novel deep convolutional neural network algorithm to achieve high-accuracy fire and smoke image detection. Instead of using traditional rectified linear units or tangent functions, we use adaptive piecewise linear units in the hidden layers of the network. We also have created a new small dataset of fire and smoke images to train and evaluate our model. To solve the overfitting problem caused by training the network on a limited dataset, we improve the number of available training images using traditional data augmentation techniques and generative adversarial networks. Experimental results show that the proposed approach achieves high accuracy and a high detection rate, as well as a very low rate of false alarms.


References | Cited By  «-- Click to see who has cited this paper

[1] Chen, Thou-Ho, Yen-Hui Yin, Shi-Feng Huang, and Yan-Ting Ye. "The smoke detection for early fire-alarming system base on video processing." Intelligent Information Hiding and Multimedia Signal Processing, pp. 427-430, 2006.
[CrossRef] [SCOPUS Times Cited 144]


[2] Töreyin, B. Ugur, Yigithan Dedeoglu, Ugur Güdükbay, and A. Enis Cetin. "Computer vision based method for real-time fire and flame detection." Pattern recognition letters 27, no. 1, pp. 49-58, 2006.
[CrossRef] [Web of Science Times Cited 254] [SCOPUS Times Cited 325]


[3] Mueller, Martin, Peter Karasev, Ivan Kolesov, and Allen Tannenbaum. "Optical flow estimation for flame detection in videos." IEEE Transactions on image processing 22, no. 7, pp.2786-2797, 2013.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 41]


[4] Bugaric, M., Jakovcevic, T., & Stipanicev, D. Computer Vision Based Measurement of Wildfire Smoke Dynamics. Advances in Electrical and Computer Engineering, 2015. Volume 15, no 1, 55-62.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[5] Celik, Turgay, Hüseyin Özkaramanli, and Hasan Demirel. "Fire and smoke detection without sensors: Image processing based approach." Signal Processing Conference, 2007 15th European, pp. 1794-1798, 2007.

[6] Zhang, Qingjie, Jiaolong Xu, Liang Xu, and Haifeng Guo. "Deep convolutional neural networks for forest fire detection." Proceedings of the 2016 International Forum on Management, Education and Information Technology Application. Atlantis Press. 2016.

[7] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems, pp. 1097-1105, 2012.
[CrossRef] [Web of Science Times Cited 347] [SCOPUS Times Cited 524]


[8] Tao, Chongyuan, Jian Zhang, and Pan Wang. "Smoke detection based on deep convolutional neural networks." In Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), 2016 International Conference on, pp. 150-153, 2016.
[CrossRef] [SCOPUS Times Cited 18]


[9] Yin, Zhijian, Boyang Wan, Feiniu Yuan, Xue Xia, and Jinting Shi. "A deep normalization and convolutional neural network for image smoke detection." IEEE Access 5, pp. 18429-18438, 2017.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 26]


[10] Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial nets." Advances in neural information processing systems, pp. 2672-2680. 2014.

[11] LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86, no. 11, pp, 2278-2324, 1998.
[CrossRef] [Web of Science Times Cited 7711] [SCOPUS Times Cited 10709]


[12] Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European conference on computer vision, pp. 818-833, 2014.
[CrossRef] [SCOPUS Times Cited 2764]


[13] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE international conference on computer vision, pp. 1026-1034. 2015.
[CrossRef] [Web of Science Times Cited 1424] [SCOPUS Times Cited 2533]


[14] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
[CrossRef] [Web of Science Times Cited 434] [SCOPUS Times Cited 11186]


[15] Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with convolutions." Cvpr, 2015.
[CrossRef] [SCOPUS Times Cited 7117]


[16] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv: 1409.1556, 2014

[17] Huang, Gao, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition, vol. 1, no. 2, p. 3. July, 2017.
[CrossRef] [Web of Science Times Cited 671] [SCOPUS Times Cited 1390]


[18] Simard, Patrice Y., David Steinkraus, and John C. Platt. "Best practices for convolutional neural networks applied to visual document analysis." ICDAR, vol. 3, pp. 958-962, 2003.
[CrossRef] [SCOPUS Times Cited 935]


[19] Zhu, Jun-Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. "Unpaired image-to-image translation using cycle-consistent adversarial networks." arXiv preprint arXiv:1703.10593, 2017.
[CrossRef] [Web of Science Times Cited 147] [SCOPUS Times Cited 545]


[20] Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. "Image-to-image translation with conditional adversarial networks." arXiv preprint, 2017.
[CrossRef] [Web of Science Times Cited 176] [SCOPUS Times Cited 728]


[21] Agostinelli, Forest, Matthew Hoffman, Peter Sadowski, and Pierre Baldi. "Learning activation functions to improve deep neural networks." arXiv preprint arXiv:1412.6830, 2014.
[CrossRef] [Web of Science Times Cited 987] [SCOPUS Times Cited 934]


[22] Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249-256, 2010.

[23] Yuan, Feiniu, Jinting Shi, Xue Xia, Yuming Fang, Zhijun Fang, and Tao Mei. "High-order local ternary patterns with locality preserving projection for smoke detection and image classification." Information Sciences 372, p.p: 225-240.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 27]


[24] Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado et al. "Tensorflow: Large-scale machine learning on heterogeneous distributed systems." arXiv preprint arXiv:1603.04467, 2016



References Weight

Web of Science® Citations for all references: 12,216 TCR
SCOPUS® Citations for all references: 39,948 TCR

Web of Science® Average Citations per reference: 489 ACR
SCOPUS® Average Citations per reference: 1,598 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-07-17 00:20 in 125 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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