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基于分数阶的多向微分算子的高炉料面轮廓自适应检测

蒋朝辉 吴巧群 桂卫华 阳春华 谢永芳

蒋朝辉, 吴巧群, 桂卫华, 阳春华, 谢永芳. 基于分数阶的多向微分算子的高炉料面轮廓自适应检测. 自动化学报, 2017, 43(12): 2115-2126. doi: 10.16383/j.aas.2017.c160621
引用本文: 蒋朝辉, 吴巧群, 桂卫华, 阳春华, 谢永芳. 基于分数阶的多向微分算子的高炉料面轮廓自适应检测. 自动化学报, 2017, 43(12): 2115-2126. doi: 10.16383/j.aas.2017.c160621
JIANG Zhao-Hui, WU Qiao-Qun, GUI Wei-Hua, YANG Chun-Hua, XIE Yong-Fang. Adaptive Detection of Blast Furnace Surface Contour with Fractional Multi-directional Differential Operator. ACTA AUTOMATICA SINICA, 2017, 43(12): 2115-2126. doi: 10.16383/j.aas.2017.c160621
Citation: JIANG Zhao-Hui, WU Qiao-Qun, GUI Wei-Hua, YANG Chun-Hua, XIE Yong-Fang. Adaptive Detection of Blast Furnace Surface Contour with Fractional Multi-directional Differential Operator. ACTA AUTOMATICA SINICA, 2017, 43(12): 2115-2126. doi: 10.16383/j.aas.2017.c160621

基于分数阶的多向微分算子的高炉料面轮廓自适应检测

doi: 10.16383/j.aas.2017.c160621
基金项目: 

高性能复杂制造国家重点实验室自主研究课题 ZZ YJKT2016-05

国家自然科学基金 61290325

国家自然科学基金 61621062

详细信息
    作者简介:

    吴巧群 中南大学信息科学与工程学院硕士研究生.主要研究方向为图像处理, 智能控制系统.E-mail:qiaoqunwu@126.com

    桂卫华 中国工程院院士, 中南大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模与优化控制, 工业大系统控制理论与应用.E-mail:gwh@mail.csu.edu.cn

    阳春华 博士, 中南大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模与优化控制, 智能自动化控制系统.E-mail:ychh@mail.csu.edu.cn

    谢永芳:XIE Yong-Fang  Ph.D., professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, and distributed robust control

    通讯作者:

    蒋朝辉 博士, 中南大学信息科学与工程学院副教授.主要研究方向为复杂工业过程建模与优化控制, 广义大系统控制理论与应用.本文通信作者.E-mail:jzh0903@csu.edu.cn

Adaptive Detection of Blast Furnace Surface Contour with Fractional Multi-directional Differential Operator

Funds: 

Independent Research Topics of State Key Labratory of High Performance Complex Manufactring ZZ YJKT2016-05

National Natural Science Foundation of China 61290325

National Natural Science Foundation of China 61621062

More Information
    Author Bio:

    Master student at the School of Information Science and Engineering, Central South University. Her research interest covers image processing and intelligent control system

    Academician of Chinese Academy of Engineering, professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, industrial large system control theory and application

    Ph.D., professor at the School of Information Science and Engineering, Central South University. Her research interest covers modeling and optimal control of complex industrial process, and intelligent automation control system

    Corresponding author: JIANG Zhao-Hui  Ph.D., associate professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, descriptor large systems control theory and application. Corresponding author of this paper
  • 摘要: 高炉料面图像含有丰富的高炉炉况信息,高炉料面轮廓能直接反映料面的凹凸起伏、煤气流分布以及炉况等信息,但高炉料面图像具有对比度低、细节不明显和有强亮斑等特点,使得高炉料面轮廓检测十分困难.本文提出一种新型的高炉料面轮廓检测方法:1)对高炉料面图像进行预处理,增强图像动态范围和图像边缘信息;2)采用分数阶的多向微分算子提取一组料面轮廓可行域;3)用自适应方法确定最佳分数阶阶次,获得可行域中最优的料面轮廓曲线;4)用改进的Canny算子对其进行修正和补偿,得到连续准确的料面轮廓曲线.理论研究和实验结果表明,该方法可准确获取平滑的高炉料面轮廓,对高炉操作人员及时有效调控布料具有很好的参考价值.
    1)  本文责任编委 胡昌华
  • 图  1  高炉料面轮廓检测过程

    Fig.  1  Blast furnace surface contour detection process

    图  2  高炉料面图像

    Fig.  2  Blast furnace material surface image

    图  3  高炉料面增强图像

    Fig.  3  Blast furnace material surface enhanced image

    图  4  分数阶微分算子推导流程图

    Fig.  4  Flowchart of deriving ractional differential operator

    图  5  3像素$\times$ 3像素邻域(${F(i, j)}$代表该像素点$\ast$的灰度值

    Fig.  5  3 pixel $\times$ 3 pixel neighborhood

    图  6  基于分数阶的多向微分算子不同分数阶阶次的轮廓检测图

    Fig.  6  Contour detection based on fractional order of multiple direction differential operators with different fractional orders

    图  7  不同分数阶阶次的评价函数及拟合曲线

    Fig.  7  The evaluation function and fitting curve of different fractional orders

    图  8  $v =0.81$时检测结果

    Fig.  8  Result of $v=0.81$

    图  9  本文算法处理结果

    Fig.  9  Detection result of ours

    图  10  Sobel分数阶微分与本文算法处理结果对比图

    Fig.  10  Contradistinction of results of Sobel fractional differential and ours

    图  11  经典算子与本文最后处理结果对比图

    Fig.  11  Contradistinction of results of classical operators and ours

    图  12  Sobel算子检测结果与标准曲线比较图

    Fig.  12  Comparison of Sobel operator detection results and standard curve

    图  13  Log算子检测结果与标准曲线比较图

    Fig.  13  Comparison of Log operator detection results and standard curve

    图  14  Canny算子检测结果与标准曲线比较图

    Fig.  14  Comparison of Canny operator detection result and standard curve

    图  15  本文算法检测结果与标准比较图

    Fig.  15  Comparison of ours and standard curves

    图  16  斜料轮廓图

    Fig.  16  Contour of blast furnace burden sloping

    图  17  高炉正常料面增强图

    Fig.  17  Normal blast furnace material surface enhanced

    图  18  正常料面轮廓图

    Fig.  18  Contour of normal blast furnace burden

    表  1  高炉料面轮廓检测评价定量指标计算值

    Table  1  The calculated value of blast furnace surface contour detection evaluation quantitative indicators

    算法 误检像素点 误检率 漏检像素点 漏检率 命中轮廓像素点 命中率 Fom (品质因数) 算法复杂度(s)
    本文算法 321 0.2668 54 0.0449 1 149 0.9551 0.8063 2.937
    Canny 2 058 1.7107 784 0.6517 419 0.3483 0.3252 1.159
    Log 2291 1.9044 780 0.6484 423 0.3516 0.2920 0.864
    Sobel 2 424 2.0150 805 0.6692 398 0.3308 0.2 839 0.800
    下载: 导出CSV
  • [1] 杨振虎. 图像处理在高炉料面可视化监控中的理论应用[硕士学位论文], 山东大学, 中国, 2011.

    Yang Zhen-Hu. Image Processing in the Visual Monitoring of High Surface Charge in the Application of the Theory[Master dissertation], Shandong University, China, 2011.
    [2] 黄月琴, 陈先中, 王田, 黄柳, 侯庆文. 基于模糊模式识别的高炉料面控制研究. 第32届中国控制会议论文. 西安, 中国, 2013. 2548-2553

    Huang Yue-Qin, Chen Xian-Zhong, Wang Tian, Huang Liu, Hou Qing-Wen. Control study based on fuzzy pattern recognition of blast furnace surface. In:Proceedings of the 32nd Chinese Control Conference. Xi'an, China, 2013. 2548-2553
    [3] Chen X Z, Liu F M, Hou Q W, Lu Y F. Industrial high-temperature radar and imaging technology in blast furnace burden distribution monitoring process. In:Proceedings of the 9th International Conference on Electronic Measurement and Instruments. Beijing, China:IEEE, 2009. 1599-1603
    [4] Koyuncu İ, Çetin Ö, Katircioǧlu M, Tuna M. Edge dedection application with FPGA based Sobel operator. In:Proceedings of the 23th Signal Processing and Communications Applications Conference (SIU). Malatya, Turkey:IEEE, 2015. 1829-1832
    [5] 文山, 李葆青.基于小波分层的多方向图像边缘检测.自动化学报, 2007, 33 (5):480-487 http://www.aas.net.cn/CN/abstract/abstract14294.shtml

    Wen Shan, Li Bao-Qing. Multidirectional image edge detection based on wavelet laid. Acta Automatica Sinica, 2007, 33 (5):480-487 http://www.aas.net.cn/CN/abstract/abstract14294.shtml
    [6] 何春, 叶永强, 姜斌, 周鑫.一种基于分数阶次微积分模板的新型边缘检测方法.自动化学报, 2012, 38(5):776-787 http://www.aas.net.cn/CN/abstract/abstract13351.shtml

    He Chun, Ye Yong-Qiang, Jiang Bin, Zhou Xin. A novel edge detection method based on fractional-order calculus mask. Acta Automatica Sinica, 2012, 38 (5):776-787 http://www.aas.net.cn/CN/abstract/abstract13351.shtml
    [7] Gonzalez R C, Woods R E. Digital Image Processing. New Jersey, USA:Prentice-Hall, 2008. 703-728
    [8] Li E S, Zhu S L, Zhu B S, Zhao Y, Xia C G, Song L H. An adaptive edge-detection method based on the Canny operator. In:Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology. Wuhan, China:IEEE, 2009. 465-469
    [9] Zhang J X, Chang W, Wu L. Edge detection based on general grey correlation and LoG operator. In:Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI). Sanya, China:IEEE, 2010. 480-483
    [10] Wang Z M, Su J Y, Zhang P. Image edge detection algorithm based onwavelet fractional differential theory. In:Proceedings of the 35th Chinese Control Conference (CCC). Chengdu, China:IEEE, 2016. 10407-10411
    [11] Gao C B, Zhou J L, Hu J R, Lang F N. Edge detection of colour image based on quaternion fractional differential. IET Image Processing, 2011, 5 (3):261-272 doi: 10.1049/iet-ipr.2009.0409
    [12] Xie Y F, Chen H, Gui W H, Liu J P, Xia J L. A novel fractional order Sobel operator based on texture images. In:Proceedings of the 30th Chinese Control Conference (CCC). Yantai, China:IEEE, 2011. 3100-3104
    [13] Mcilhagga W. The Canny edge detector revisited. International Journal of Computer Vision, 2011, 91 (3):251-261 doi: 10.1007/s11263-010-0392-0
    [14] Hao G, Min L, Feng H. Improved self-adaptive edge detection method based on Canny. In:Proceedings of the 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Hangzhou, China:IEEE, 2013. 527-530
    [15] Yuan L Y, Xu X. Adaptive image edge detection algorithm based on Canny operator. In:Proceedings of the 4th International Conference on Advanced Information Technology and Sensor Application (AITS). Harbin, China:IEEE, 2015. 28-31
    [16] Chen Z P, Jiang Z H, Gui W H, Yang C H. A novel device for optical imaging of blast furnace burden surface:parallel low-light-loss backlight high-temperature industrial endoscope. IEEE Sensors Journal, 2016, 16 (17):6703-6717 doi: 10.1109/JSEN.2016.2587729
    [17] Yoon H, Han Y, Hahn H. Image contrast enhancement based sub-histogram equalization technique without over-equalization noise. International Journal of Electrical and Electronics Engineering, 2009, 3 (6):323-329 http://or.nsfc.gov.cn/bitstream/00001903-5/334447/1/1000013234093.pdf
    [18] Pu Y F, Wang W X, Zhou J L, Wang Y Y, Jia H D. Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation. Science in China Series F:Information Sciences, 2008, 51 (9):1319-1339 doi: 10.1007/s11432-008-0098-x
    [19] Chen X H, Fei X D. Improving edge-detection algorithm based on fractional differential approach. In:Proceedings of the 2012 International Conference on Image, Vision and Computing (ICIVC 2012). Singapore, Singapore:IACSIT Press, 2012. 1-6
    [20] 蒋伟, 陈辉.基于分数阶微分和Sobel算子的边缘检测新模型.计算机工程与应用, 2012, 48 (4):182-185 http://www.wenkuxiazai.com/doc/cd293b03763231126edb11f7.html

    Jiang Wei, Chen Hui. New edge detection model based on fractional differential and Sobel operator. Computer Engineering and Applications, 2012, 48 (4):182-185 http://www.wenkuxiazai.com/doc/cd293b03763231126edb11f7.html
    [21] Pan D F, Wang B. An improved Canny algorithm. In:Proceedings of the 27th Chinese Control Conference. Kunming, China:IEEE, 2008. 456-459
    [22] Xie Y N, Zhu Z, Zhang X G, Wang Q. An adaptive median filter using local texture information in images. In:Proceedings of the 13th IEEE/ACIS International Conference on Computer and Information Science (ICIS). Taiyuan, China:IEEE, 2014. 177-180
    [23] Panetta K, Gao C, Agaian S, Nercessian S. A new reference-based edge map quality measure. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46 (11):1505-1517 doi: 10.1109/TSMC.2015.2503386
    [24] 陈宏希.基于品质因数的边缘检测算子性能优劣客观评价研究.自动化与仪器仪表, 2015, (8):8-10 http://www.cnki.com.cn/Article/CJFDTotal-ZDYY201508004.htm

    Chen Hong-Xi. Study on the objective evaluation of the performance of edge detection operator based on the quality factor. Automation and Instrumentation, 2015, (8):8-10 http://www.cnki.com.cn/Article/CJFDTotal-ZDYY201508004.htm
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出版历程
  • 收稿日期:  2016-09-06
  • 录用日期:  2016-12-27
  • 刊出日期:  2017-12-20

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