Shape-based infrared image leakage gas detection method
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摘要: 针对工业生产中泄漏气体导致的爆炸和火灾问题, 提出一种基于形状和SVM分类的红外图像泄漏气体检测方法。采用泄漏气体和干扰物红外图像样本的形状特征训练SVM分类器, 通过对红外图像序列采用基于背景差分的运动检测得到候选目标区域, 再对候选目标区域提取其形状特征, 最后使用SVM分类器进行判别, 从而得到最终的检测结果。使用乙烯气体泄漏仿真数据进行实验, 检测率最高可达98%, 结果表明, 采用该方法可以有效检测泄漏气体, 相比其他方法, 极大地减少了干扰物造成的误检。Abstract: Aiming at the explosion and fire caused by leakage gas in industrial production, an infrared image leakage gas detection method based on shape and support vector machine(SVM) is proposed. The SVM classifier is trained by using the shape features of the infrared image sample of the leaking gas and the interfering object. The candidate target region is obtained by using the background difference-based motion detection for the infrared image sequence, and then the shape feature is extracted from the candidate target region, and finally the SVM classifier is used to obtain the final detection result. Experiments were carried out using ethylene gas leakage simulation data, and the detection rate was up to 98%. The results show that this method can effectively detect the leakage gas, which greatly reduces the false detection caused by the interference.
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Keywords:
- leak gas detection /
- shape feature /
- support vector machine(SVM) /
- infrared image
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图 1 本文所提出的算法流程及其对应的关键中间结果
Figure 1. Flow chart of algorithm proposed and its corresponding key intermediate results
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[1] 裴昱, 陈远鸣, 卞晓阳, 等.基于RBF神经网络气压补偿的非色散红外SF_6气体传感器[J].应用光学, 2018, 39(03):366-372. http://www.yygx.net/CN/abstract/abstract11118.shtml PEI Yu, CHEN Yuanming, BIAN Xiaoyang, et al. Non-dispersion infrared SF_6 gas sensor with air pressure compensation based on RBF neural network[J]. Journal of Applied Optics, 2018, 39(03):366-372. http://www.yygx.net/CN/abstract/abstract11118.shtml
[2] 谭雨婷, 李家琨, 金伟其, 等.气体泄漏的单点探测器与红外成像检测的灵敏度模拟分析[J].红外与激光工程, 2014, 43(08):2489-2495. doi: 10.3969/j.issn.1007-2276.2014.08.017 TAN Yuting, LI Jiakun, JIN Weiqi, et al. Model analysis of the sensitivity of single-point sensor and IRFPAdetectors used in gas leakage detection[J]. Infrared and Laser Engineering, 2014, 43(08):2489-2495. doi: 10.3969/j.issn.1007-2276.2014.08.017
[3] MURVAY P S, SILEA I. A survey on gas leak detection and localization techniques[J].Journal of Loss Prevention in the Process Industries, 2012, 25(6):966-973. doi: 10.1016/j.jlp.2012.05.010
[4] ZIVKOVIC Z. Improved adaptive Gaussian mixture model for background subtraction[J].Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. IEEE, 2004, 2: 28-31.
[5] PICCARDI M. Background subtraction techniques: a review[J].Systems, man and cybernetics, 2004 IEEE international conference on. IEEE, 2004, 4: 3099-3104.
[6] APPANA D K, ISLAM R, KHAN S A, et al. A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems[J]. Information Sciences, 2017, 418:91-101. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e6c037e1ce492ed7c523a66d2dda67b5
[7] OJO J A, OLADOSU J A. Effective smoke detection ising spatial-temporal energy and weber local descriptors in three orthogonal planes (WLD-TOP)[J]. Journal of Computer Science and Technology, 2018, 18(01):e05-e05.
[8] YE W, ZHAO J, WANG S, et al. Dynamic texture based smoke detection using surfacelet transform and HMT model[J]. Fire Safety Journal, 2015, 73:91-101. doi: 10.1016/j.firesaf.2015.03.001
[9] YUAN F. A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection[J]. Pattern Recognition, 2012, 45(12):4326-4336. doi: 10.1016/j.patcog.2012.06.008
[10] ZHAO Y, LI Q, GU Z. Early smoke detection of forest fire video using CS Adaboost algorithm[J]. Optik-International Journal for Light and Electron Optics, 2015, 126(19):2121-2124. doi: 10.1016/j.ijleo.2015.05.082
[11] LI S, WANG B, GONG L, et al. A novel smoke detection algorithm based on MSER tracking[C]//Control and Decision Conference (CCDC), 2015 27th Chinese. IEEE, 2015: 5676-5681.
[12] FILONENKO A, HERNNDEZ D C, JO K H. Real-time smoke detection for surveillance[C]//Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on. IEEE, 2015: 568-571.
[13] 王小川, 史峰, 郁磊, 等. MATLAB神经网络43个案例分析[J].北京: 北京航空航天大学出版社, 2013. WANG Xiaochuan, SHI Feng, YU Lei, et al. MATLAB neural network 43 case studies[J]. Beijing: Beihang University Press, 2013.
[14] HSU C W, CHANG C C, LIN C J. A practical guide to support vector classification[EB/OL].[2016-05-19]. https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
[15] CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J]. ACM transactions on intelligent systems and technology (TIST), 2011, 2(3):27. http://d.old.wanfangdata.com.cn/Periodical/jdq201315008
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