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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
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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

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