基于相似度阈值模糊聚类的红外区域提取方法

郭锋, 郑雷, 葛黄徐, 严碧武, 郭一凡

郭锋, 郑雷, 葛黄徐, 严碧武, 郭一凡. 基于相似度阈值模糊聚类的红外区域提取方法[J]. 红外技术, 2022, 44(8): 863-869.
引用本文: 郭锋, 郑雷, 葛黄徐, 严碧武, 郭一凡. 基于相似度阈值模糊聚类的红外区域提取方法[J]. 红外技术, 2022, 44(8): 863-869.
GUO Feng, ZHENG Lei, GE Huangxu, YAN Biwu, GUO Yifan. Infrared Image Segmentation Method Based on Fuzzy Clustering with Similarity Thresholding[J]. Infrared Technology , 2022, 44(8): 863-869.
Citation: GUO Feng, ZHENG Lei, GE Huangxu, YAN Biwu, GUO Yifan. Infrared Image Segmentation Method Based on Fuzzy Clustering with Similarity Thresholding[J]. Infrared Technology , 2022, 44(8): 863-869.

基于相似度阈值模糊聚类的红外区域提取方法

基金项目: 

国家电网公司总部科技项目 521104180025

详细信息
    作者简介:

    郭锋(1975-),男,高级工程师,硕士,研究方向为输变电设备远程运维与管理。E-mail:maxwell201904@163.com

  • 中图分类号: TP391

Infrared Image Segmentation Method Based on Fuzzy Clustering with Similarity Thresholding

  • 摘要: 针对输电线路电力设备红外图像热故障区域检测,提出采用一种基于相似度阈值的模糊聚类热故障区域提取方法。在该方法中,改进了传统模糊均值聚类算法的迭代求解方式,采用一种阈值化模糊聚类;其次,通过对目标区域局部邻域像素的相似度聚类分析,并结合其隶属度的计算,确保局部邻域像素在聚类上的相似性。同时,引入了最大相似度阈值准则简化均值的设置以及自高向低的迭代方式,从而提升区域提取效率。最后通过真实输电线路电气设备红外故障图像测试,验证了文中所提方法的有效性和适用性。
    Abstract: This paper presents a fuzzy clustering method based on similarity thresholding to detect an overheating fault region from an infrared image of a transmission line. In this method, the original iteration mechanism of fuzzy clustering was improved and a thresholding fuzzy clustering model was built. Thus, a fuzzy member was utilized to measure the neighboring pixels t by conducting cluster analysis on the object region with local neighboring pixels. This ensured similarity during the clustering of the local neighboring pixels into the cluster center. In addition, the maximum similarity thresholding rule was applied to determine the final thresholding using the strategy of thresholding from top to bottom, thus improving the efficiency of the method in obtaining the final region of interest in the infrared image using fuzzy clustering. Finally, experimental results on infrared images of transmission lines show that the good performance of the proposed method and that the proposed method is suitable for fault detection in transmission lines.
  • 图  1   局部区域示意图

    Figure  1.   The illustration of neighboring region

    图  2   阈值化迭代流程框图

    Figure  2.   The flowchart of thresholding

    图  3   红外图像

    Figure  3.   Real-world infrared images

    图  4   Otsu方法分割结果

    Figure  4.   Segmentation results of Otsu method

    图  5   MST方法分割结果

    Figure  5.   Segmentation results of MST method

    图  6   FCM聚类分割结果

    Figure  6.   The segmentation results of FCM method

    图  7   Meanshift方法最终分割结果

    Figure  7.   Final segmentation results of Meanshift clustering method

    图  8   本文方法分割结果

    Figure  8.   Segmentation results of our method

    图  9   红外梯度结果图

    Figure  9.   The gradient results of infrared images

    图  10   MST下相似度结果

    Figure  10.   Similarity of MST method

    表  1   Otsu和MST方法阈值

    Table  1   Thresholding of MST and Otsu

    Method Image 1 Image 2 Image 3 Image 4 Image 5
    Otsu 21 59 31 57 53
    MST 15 45 22 41 84
    下载: 导出CSV

    表  2   最终迭代后的均值

    Table  2   Mean value of final results

    Image 1 Image 2 Image 3 Image 4 Image 5
    FCM v0 1.0603 21.05 1.01730 5.7834 24.3979
    v1 41.3490 96.8447 53.8848 31.1222 84.1881
    Ours v0 12.1560 65.2512 30.4860 14.9498 48.7644
    v1 130.9086 133.1862 147.4263 129.3911 209.7107
    下载: 导出CSV

    表  3   相似度评价结果

    Table  3   Evaluation of similarity

    Image 1 Image 2 Image 3 Image 4 Image 5
    MST 0.5120 0.3872 0.3433 0.4815 0.3552
    Ours 0.3430 0.2419 0.2445 0.3515 0.1962
    下载: 导出CSV

    表  4   不同方法运行时长对比

    Table  4   Comparison of time speeding methods in evaluation s

    Image 1 Image 2 Image 3 Image 4 Image 5
    Otsu 0.1402 0.0022 0.1076 0.0042 0.2027
    MST 3.7802 0.2007 3.4117 0.9979 2.32
    FCM 5.6357 0.9010 13.6103 7.7979 15.1982
    Meanshift 5.5414 0.2870 7.5225 1.1907 12.3957
    Ours 4.3923 0.2682 5.8280 0.6663 3.1632
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-01-20
  • 修回日期:  2021-02-23
  • 刊出日期:  2022-08-19

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