留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于OTSU和区域生长的电厂管道缺陷检测与分割

彭道刚 尹磊 戚尔江 胡捷 杨晓伟

彭道刚, 尹磊, 戚尔江, 胡捷, 杨晓伟. 基于OTSU和区域生长的电厂管道缺陷检测与分割[J]. 红外技术, 2021, 43(5): 502-509.
引用本文: 彭道刚, 尹磊, 戚尔江, 胡捷, 杨晓伟. 基于OTSU和区域生长的电厂管道缺陷检测与分割[J]. 红外技术, 2021, 43(5): 502-509.
PENG Daogang, YIN Lei, QI Erjiang, HU Jie, YANG Xiaowei. Power Plant Pipeline Defect Detection and Segmentation Based on Otsu's and Region Growing Algorithms[J]. Infrared Technology , 2021, 43(5): 502-509.
Citation: PENG Daogang, YIN Lei, QI Erjiang, HU Jie, YANG Xiaowei. Power Plant Pipeline Defect Detection and Segmentation Based on Otsu's and Region Growing Algorithms[J]. Infrared Technology , 2021, 43(5): 502-509.

基于OTSU和区域生长的电厂管道缺陷检测与分割

详细信息
    作者简介:

    彭道刚(1977-),男,教授,博士,从事智能发电、能源互联网、电力巡检机器人研究。E-mail:pengdaogang@126.com

  • 中图分类号: TP391.41

Power Plant Pipeline Defect Detection and Segmentation Based on Otsu's and Region Growing Algorithms

  • 摘要: 针对电厂高温管道红外图像背景复杂、干扰较多的特点,结合电厂巡检机器人系统对图像处理算法的需求,提出了基于改进二维最大类间方差法(OTSU)和区域生长法的电厂高温管道缺陷定位与分割方法。将红外图像灰度化后,通过改进二维OTSU进行预分割,提取出管道区域;基于管道区域灰度直方图,结合邻域灰度均值,实现多种子点的自动检测与定位;采用基于生长区域灰度均值和标准差的自适应阈值以及基于Prewitt算子的梯度幅值改进的生长准则完成缺陷区域的分割。实验证明,所提算法不仅能实现电厂高温管道多缺陷自动检测与定位,而且能精确地提取出缺陷区域,准确性高且具有良好的实时性。
  • 图  1  缺陷检测系统流程图

    Figure  1.  Flow chart of defect detection system

    图  2  二维直方图

    Figure  2.  Two-dimensional histogram

    图  3  区域生长法流程图

    Figure  3.  Flow chart of region growing algorithm

    图  4  多种子点自动选取流程图

    Figure  4.  Flow chart for automatic selection of multiple seed points

    图  5  像素3×3邻域图

    Figure  5.  Pixel neighborhood map

    图  6  电厂高温管道红外图像

    Figure  6.  Infrared image of high temperature pipeline in a power plant

    图  7  本文方法实验结果图

    Figure  7.  The experimental results of this paper's method

    图  8  3种算法结果对比图

    Figure  8.  Comparison of three algorithm results

    表  1  缺陷区域像素点个数表

    Table  1.   Number of pixels in defect area

    Algorithm Defect image Defect one Defect two Defect three Defect four
    Algorithm of this article Scene one 929 - - -
    Scene two 796 489 - -
    Scene three 249 129 109 96
    Traditional regional growth algorithm Scene one 930 - - -
    Scene two 827 551 - -
    Scene three 117 79 59 80
    Algorithm of literature [10] Scene one 941 - - -
    Scene two 776 493 - -
    Scene three 226 118 106 95
    下载: 导出CSV

    表  2  算法性能对比表

    Table  2.   Algorithm performance comparison table

    Algorithm False detection Seed point selection Time/(s/sheet)
    Algorithm of this article No Auto 0.331
    Traditional regional growth algorithm No Manual ≥3.5
    Algorithm of literature [10] Yes Auto 0.254
    下载: 导出CSV
  • [1] 王丞浩. 基于物联网的电厂智能巡检系统移动端设计与实现[D]. 吉林: 东北电力大学, 2019.

    WANG Chenghao. Design and Implement of Mobile Terminal Power Plant Intelligent Patrol System Based on IOT[D]. Jilin: Northeast Electric Power University, 2019.
    [2] 华志刚, 郭荣, 汪勇. 燃煤智能发电的关键技术[J]. 中国电力, 2018, 51(10): 8-16. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDL201810004.htm

    HUA Zhigang, GUO Rong, WANG Yong. Key technologies for intelligent coal-fired power generation[J]. Electric Power, 2018, 51(10): 8-16. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDL201810004.htm
    [3] 张燕东, 田磊, 李茂清, 等. 智能巡检机器人系统在火力发电行业的应用研发及示范[J]. 中国电力, 2017, 50(10): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDL201710001.htm

    ZHANG Yandong, TIAN Lei, LI Maoqing, et al. Application and development of intelligent inspection robot system in thermal power plant[J]. Electric Power, 2017, 50(10): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDL201710001.htm
    [4] 徐蔚波, 刘颖, 章浩伟. 基于区域生长的图像分割研究进展[J]. 北京生物医学工程, 2017, 36(3): 317-322. doi:  10.3969/j.issn.1002-3208.2017.03.16.

    XU Weibo, LIU Ying, ZHANG Haowei. Research progress in image segmentation based on region growing[J]. Beijing Biomedical Engineering, 2017, 36(3): 317-322. doi:  10.3969/j.issn.1002-3208.2017.03.16.
    [5] Jain Preetha M M S, Padmasuresh L, Bosco M J. Firefly based region growing and region merging for image segmentation[C]//2016 International Conference on Emerging Technological Trends (ICETT), 2016: 1-9.
    [6] 彭双, 肖昌炎. 结合区域生长与模糊连接度的肺气管树分割[J]. 计算机工程与应用, 2016, 52(13): 201-205. doi:  10.3778/j.issn.1002-8331.1407-0623

    PENG Shuang, XIAO Changyan. Segmentation of pulmonary airway tree by combining region growing and fuzzy connectedness[J]. Computer Engineering and Applications, 2016, 52(13): 201-205. doi:  10.3778/j.issn.1002-8331.1407-0623
    [7] Senthilkumar B, Umamaheswari G, Karthik J. A novel region growing segmentation algorithm for the detection of breast cancer[C]//2010IEEE International Conference on Computational Intelligence and Computing Research, 2010: 1-4.
    [8] SONG L, LV Y, YANG B, et al. Segmentation of breast masses using adaptive region growing[C]//Ulaanbaatar, Ifost, 2013: 77-81.
    [9] 倪豪, 郑慧峰, 王月兵, 等. 基于自动种子区域生长的超声B图像缺陷分割方法[J]. 计量学报, 2018, 39(6): 878-883. doi:  10.3969/j.issn.1000-1158.2018.06.24

    NI Hao, ZHEN Huifeng, WANG Yuebing, et al. Ultrasonic B image defect segmentation Method Based on automatic seeded region growing[J]. Acta Metrologica Sinica, 2018, 39(6): 878-883. doi:  10.3969/j.issn.1000-1158.2018.06.24
    [10] 李小磊, 曾曙光, 郑胜, 等. 基于滑动滤波和自动区域生长的陶瓷瓦表面裂纹检测[J]. 激光与光电子学进展, 2019, 56(21): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201921006.htm

    LI Xiaolei, ZENG Shuguang, ZHENG Sheng, et al. Surface crack detection of ceramic tile based on sliding filter and automatic region growth[J]. Laser & Optoelectronics Progress, 2019, 56(21): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201921006.htm
    [11] 施兢业, 刘俊. 基于改进区域生长法的电力设备红外图像分割[J]. 光学技术, 2017, 43(4): 381-384. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS201704019.htm

    SHI Jingye, LIU Jun. Metation based on modified region growing algorithm[J]. Optical Technique, 2017, 43(4): 381-384. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS201704019.htm
    [12] 胡淋波, 姚建刚, 孔维辉, 等. 基于红外图像的高压绝缘子串自动定位方法[J]. 红外技术, 2015, 37(12): 1047-1051. http://hwjs.nvir.cn/article/id/hwjs201512011

    HU Linbo, YAO Jiangang, KONG Weihui, et al. High voltage insulator string automatic location method based on infrared image[J]. Infrared Technology, 2015, 37(12): 1047-1051. http://hwjs.nvir.cn/article/id/hwjs201512011
    [13] 宋银龙. 基于二维Otsu和模糊聚类的图像分割的研究及应用[D]. 合肥: 合肥工业大学, 2012.

    SONG Yinlong. Research and application of image segmentation based on two-dimensional otsu and fuzzy clustering[D]. Hefei: Hefei University of Technology, 2012.
    [14] 倪伟传, 许志明, 刘少江, 等. 复杂环境下的自适应红外目标分割算法[J]. 红外技术, 2019, 41(4): 357-363. http://hwjs.nvir.cn/article/id/hwjs201904010

    NI Weichuan, XU Zhiming, LIU Shaojiang, et al. Adaptive Infrared Target Segmentation Algorithm in Complex Environment[J]. Infrared Technology, 2019, 41(4): 357-363. http://hwjs.nvir.cn/article/id/hwjs201904010
    [15] SHAO L, ZHANG Y, LI J, et al. Research on High Temperature Region of Infrared Pipeline Image Based on Improved Two-Dimensional-Otsu[J]. Spectroscopy and Spectral Analysis, 2019, 39(5): 1637-1642.
    [16] 彭启伟, 罗旺, 冯敏, 等. 改进二维Otsu法和果蝇算法结合的图像分割方法[J]. 计算机应用, 2017, 37(S2): 193-197. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2017S2047.htm

    PENG Qiwei, LUO Wang, FENG Min, et al. Novel method for image segmentation based on improved two-dimensional Otsu and fruit fly algorithm[J]. Journal of Computer Applications, 2017, 37(S2): 193-197. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2017S2047.htm
    [17] 周云燕, 杨坤涛, 黄鹰. 基于最小类内离散度的改进Otsu分割方法的研究[J]. 华中科技大学学报: 自然科学版, 2007, 35(2): 101-103. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG200702030.htm

    ZHOU Yunyan, YANG Kuntao, HUANG Ying. Improved Otsu thresholding based on minimum inner-cluster variance[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2007, 35(2): 101-103. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG200702030.htm
    [18] HUANG C, LIU Q, LI X. Color image segmentation by seeded region growing and region merging[C]//2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010: 533-536.
    [19] YANG L, WU X, ZHAO D, et al. An improved Prewitt algorithm for edge detection based on noised image[C]//20114th International Congress on Image and Signal Processing, 2011: 1197-1200.
  • 加载中
图(8) / 表(2)
计量
  • 文章访问数:  135
  • HTML全文浏览量:  92
  • PDF下载量:  23
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-08-16
  • 修回日期:  2020-10-24
  • 刊出日期:  2021-05-22

目录

    /

    返回文章
    返回