Ultraviolet Image Parameters Extraction of Insulation Surface Discharge Based on Improved C-V Model

王丰华; 刘国坚; 张宏钊; 黄荣辉; 刘亚东

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Ultraviolet Image Parameters Extraction of Insulation Surface Discharge Based on Improved C-V Model

更新时间：2025-12-08

- Ultraviolet Image Parameters Extraction of Insulation Surface Discharge Based on Improved C-V Model
- Vol. 44, Issue 8, Pages: 2525-2532(2018)
- 作者机构：
  
  1. 电力传输与功率变换控制教育部重点实验室(上海交通大学)
  2. 上海电力学院电气工程学院
  3. 深圳供电局有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 August 2018，
  
  Published：2018
- 稿件说明：
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王丰华, 刘国坚, 张宏钊, et al. Ultraviolet Image Parameters Extraction of Insulation Surface Discharge Based on Improved C-V Model[J]. 2018, 44(8): 2525-2532.
DOI：

王丰华, 刘国坚, 张宏钊, et al. Ultraviolet Image Parameters Extraction of Insulation Surface Discharge Based on Improved C-V Model[J]. 2018, 44(8): 2525-2532. DOI：

摘要

为实现紫外（UV）成像技术检测绝缘子缺陷时产生的紫外图像中放电区域的自动准确分割

提出了一种基于改进Chan-Vese（C-V）模型的放电光斑提取方法。通过引入符号距离保持项、用水平集函数梯度的模替代Dirac函数以及简化模型参数来对现有C-V模型进行了改进

然后将其用于获取绝缘子表面放电紫外图像的放电光斑

并给出了基于常规边缘检测算子法、数学形态学的紫外图像分割结果。结果表明:改进后的C-V模型能更为快速准确地分割出紫外成像的目标光斑

且具有较好的抗干扰能力。与现有方法相比较

使用改进的C-V模型来提取放电光斑的准确度提高了5%

且对C-V模型中关键参数的依赖性较小

非常适合于紫外图像特征量的提取。研究结果可为输电线路外绝缘的有效检测提供重要依据。

Abstract

To segment the discharge area of insulator in ultraviolet（UV） image produced by ultraviolet imaging technology automatically and accurately

we proposed an improved method based on an improved Chan-Vese（C-V） model to analyze the discharge area

where the existing C-V model is improved by adding a penalized energy term

displacing the Dirac function with the norm of level set function gradient

and simplifying amount of parameters. Then

the method was used to obtain the discharge spot of the ultraviolet image of the insulator surface

and the UV image segmentation results based on the conventional edge detection algorithm and mathematical morphology were also given. The research results indicate that the proposed method can segment the discharge area faster and more accurately with stronger anti-interference ability. The accuracy of the improved C-V model is 5% higher than that of current methods

and the dependency on key parameters is lower. The proposed C-V model is excellent for segmentation of ultraviolet image parameters. The research results can provide an important reference for the effective detection of the insulation for the transmission line.

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