基于目标检测算法的复合绝缘子表面憎水性智能判别方法

何潇; 孙剑; 邓冶强; 王羽; 付忠祥; 姜炯挺; 文习山

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基于目标检测算法的复合绝缘子表面憎水性智能判别方法

更新时间：2025-06-18

- 基于目标检测算法的复合绝缘子表面憎水性智能判别方法
- Intelligent Classfication Method of Composite Insulator Surface Hydrophobicity Based on Target Detection Algorithm
- 高压电器 2025年页码：1-11
- 作者机构：
  
  1.武汉大学电气与自动化学院，武汉　 430072
  2.国网老河口供电公司，湖北　襄阳　 420682
  3.国网宁波供电公司，浙江　宁波　 315010
- 作者简介：
  
  何潇（1999—），男，硕士研究生，研究方向为防雷与外绝缘（E-mail：He_xiao@whu.edu.cn）。
- 基金信息：
  
  国家自然科学基金青年项目(52207168);国家自然科学基金面上项目(51977162)
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何潇, 孙剑, 邓冶强, 等. 基于目标检测算法的复合绝缘子表面憎水性智能判别方法[J/OL]. 高压电器, 2025,1-11.

HE Xiao, SUN Jian, DENG Yeqiang, et al. Intelligent Classfication Method of Composite Insulator Surface Hydrophobicity Based on Target Detection Algorithm[J/OL]. High voltage apparatus, 2025, 1-11.
何潇, 孙剑, 邓冶强, 等. 基于目标检测算法的复合绝缘子表面憎水性智能判别方法[J/OL]. 高压电器, 2025,1-11. DOI：

HE Xiao, SUN Jian, DENG Yeqiang, et al. Intelligent Classfication Method of Composite Insulator Surface Hydrophobicity Based on Target Detection Algorithm[J/OL]. High voltage apparatus, 2025, 1-11. DOI：

摘要

目前基于卷积神经网络将复合绝缘子喷水图像进行整体憎水性分类的方法对于图像局部的憎水性关注度不足，因此本文提出了一种基于目标检测算法的复合绝缘子表面憎水性判别方法。首先取样不同形态的喷水图片共5 800张，根据水珠形貌和接触角提出了单独针对水珠的分类标准。之后采用以SE-Resnet为骨架网络的Faster R-CNN对表面水珠进行分类，并获得了基于目标检测算法的21个水珠局部特征参数。为了兼顾图片全局特性，同时基于数字图像处理构建了12个与水珠亮斑面积和形态相关的全局参数。最后通过特征筛选，建立了基于BP神经网络的26参数憎水性等级自动判别模型，实现了检测过程的自动化。在±1误差允许范围内，模型判别准确率达到了99.13%。

Abstract

The current method of classifying the overall hydrophobicity of composite insulator spray images based on convolutional neural network does not pay enough attention to the local water repellency of the images

so this paper proposes a method to discriminate the water repellency of composite insulator surface based on target detection algorithm. Firstly

5 800 water spray images with different morphologies are sampled

and the classification criteria for water droplets are proposed according to their morphology. After that

Faster R-CNN with SE-Resnet as the backbone network is used to classify the surface water droplets

and 21 local feature parameters of water droplets based on the target detection algorithm are obtained. In order to take into account the global characteristics of the image

12 global parameters related to the bright spots and morphology of water droplets are also constructed based on digital image processing. Finally

a BP neural network-based 26-parameter hydrophobicity automatic classification model is constructed after feature screening

which realizes the automatic classification process. The accuracy of the model reached 99.13% with ±1 error tolerance.

关键词

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references

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