基于灰度聚类的光伏红外热斑图像分割方法

谢七月; 孙武彪; 申忠利; 周育才

doi:10.19912/j.0254-0096.tynxb.2022-1809

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (9) : 117-124. DOI: 10.19912/j.0254-0096.tynxb.2022-1809

基于灰度聚类的光伏红外热斑图像分割方法

谢七月¹, 孙武彪², 申忠利¹, 周育才¹

作者信息 +

PHOTOVOLTAIC INFRARED HOT SPOT IMAGE SEGMENTATION METHOD BASED ON GRAY CLUSTERING

Xie Qiyue¹, Sun Wubiao², Shen Zhongli¹, Zhou Yucai¹

Author information +

文章历史 +

摘要

针对光伏红外热斑检测问题,提出一种曲线拟合结合图像聚类的热斑红外图像处理方法。首先,将图像灰度变换处理后,采用高斯最小二乘拟合确定聚类中心点;然后,针对传统模糊C均值噪声鲁棒性较差的特点,加入邻域空间影响以及用核距离代替欧式距离的模糊C均值算法对热斑图像进行聚类;最后,根据拟合图进行灰度多阈值分割。实验结果表明：该方法能将光伏组件损坏程度进行量化,区域进行分层,抑制红外图像噪声,提高检测热斑效率的同时,分割准确率可达86%以上。

Abstract

For the photovoltaic infrared hot spot detection problem, this paper proposes a curve fitting combined with image clustering of the hot spot infrared image processing methods. Firstly, after image gray scale transformation, Gaussian least square fitting was used to determine the clustering center. In view of the poor robustness of traditional FCM noise, the hot spot images were clustered by adding the influence of neighborhood space and substituting the Euclidean distance with the kernel distance. Finally, the gray multi-threshold segmentation was carried out according to the fitting graph. The experimental results show that the method can quantify the damage degree of photovoltaic modules, regional stratification, suppress infrared image noise, improve the efficiency of hot spot detection with the segmentation accuracy of more than 86%.

导出引用

谢七月, 孙武彪, 申忠利, 周育才. 基于灰度聚类的光伏红外热斑图像分割方法[J]. 太阳能学报. 2023, 44(9): 117-124 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1809

Xie Qiyue, Sun Wubiao, Shen Zhongli, Zhou Yucai. PHOTOVOLTAIC INFRARED HOT SPOT IMAGE SEGMENTATION METHOD BASED ON GRAY CLUSTERING[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 117-124 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1809

中图分类号： TK514

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