Live Working Insulation Blanket Surface Defect Recognition Based on Improved YOLOv8

WU Tian; WANG Mingxin; TANG Pan; ZHAO Wenjun; FANG Chunhua; PU Ziheng

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Live Working Insulation Blanket Surface Defect Recognition Based on Improved YOLOv8

更新时间：2025-03-31

- Live Working Insulation Blanket Surface Defect Recognition Based on Improved YOLOv8
- Moren Journal (2025)
- 作者机构：
  
  1.三峡大学电气与新能源学院,湖北省宜昌市443002
  2.湖北省输电线路工程技术研究中心（三峡大学）,湖北省宜昌市443002
  3.中国电力科学研究院有限公司电网环境保护国家重点实验室,湖北省武汉市430072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51807110)
- DOI：
  CLC：
- Received：29 July 2024，
  
  Revised：11 October 2024，
  
  Accepted：15 October 2024，
- 稿件说明：
移动端阅览
WU Tian, WANG Mingxin, TANG Pan, et al. Live Working Insulation Blanket Surface Defect Recognition Based on Improved YOLOv8[J/OL]. Moren journal, 2025.
DOI：

WU Tian, WANG Mingxin, TANG Pan, et al. Live Working Insulation Blanket Surface Defect Recognition Based on Improved YOLOv8[J/OL]. Moren journal, 2025. DOI：

摘要

绝缘毯表面缺陷对其绝缘性能和使用寿命有严重影响，针对现有检测方法面对不规则缺陷以及绝缘毯表面纹理干扰造成的检测效率和准确率低的问题，本文提出一种基于改进YOLOv8的绝缘毯缺陷检测算法，引入SPD-Conv卷积神经网络，解决小目标检测特征遗漏问题；引入Deformable-LKA注意力机制自适应调整卷积核大小和形状，增强特征提取能力；针对缺陷形状不规则的问题，改进YOLOv8中的损失函数为Shape-IoU，减少标注框的影响；同时开展模拟缺陷绝缘毯的沿面放电试验，获取不同缺陷对绝缘性能的影响。实验结果表明：绝缘毯表面不同类型缺陷对沿面放电特性影响差异明显，改进模型在扩充数据集上的识别率为94.8%，相比原模型在检测速度和精度上都有一定的提升，为带电作业绝缘毯表面缺陷检测提供参考。

Abstract

Surface defects of insulating blankets can severely impact their insulating performance and service life. To address the low detection efficiency and accuracy caused by irregular defects and interference from the surface texture of insulating blankets

this paper proposes an improved YOLOv8 algorithm for detecting defects in insulating blankets. The algorithm introduces an SPD-Conv convolutional neural network to address the issue of feature omission in small target detection. It incorporates the Deformable-LKA attention mechanism to adaptively adjust the size and shape of the convolutional kernel

enhancing the capability to extract features. To tackle the issue of irregular defect shapes

the loss function in YOLOv8 is improved to Shape-IoU

reducing the impact of annotation boxes. Additionally

a surface discharge test is conducted on simulated defective insulating blankets to quantify the impact of different defects on insulating performance. Experimental results indicate that different types of surface defects on insulating blankets significantly affect surface discharge characteristics. The improved model achieves a recognition rate of 94.8% on the expanded dataset

showing certain improvements in detection speed and accuracy compared to the original model

providing a reference for the detection of surface defects on insulating blankets used in live working.

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references

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