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基于U-net和YOLOv4的绝缘子图像分割与缺陷检测

唐小煜 黄进波 冯洁文 陈锡和

唐小煜, 黄进波, 冯洁文, 陈锡和. 基于U-net和YOLOv4的绝缘子图像分割与缺陷检测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088
引用本文: 唐小煜, 黄进波, 冯洁文, 陈锡和. 基于U-net和YOLOv4的绝缘子图像分割与缺陷检测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088
TANG Xiaoyu, HUANG Jinbo, FENG Jiewen, CHEN Xihe. Image Segmentation and Defect Detection of Insulators Based on U-net and YOLOv4[J]. Journal of South China normal University (Natural Science Edition), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088
Citation: TANG Xiaoyu, HUANG Jinbo, FENG Jiewen, CHEN Xihe. Image Segmentation and Defect Detection of Insulators Based on U-net and YOLOv4[J]. Journal of South China normal University (Natural Science Edition), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088

基于U-net和YOLOv4的绝缘子图像分割与缺陷检测

doi: 10.6054/j.jscnun.2020088
基金项目: 

国家自然科学基金项目 61371176

广州市高校创新创业教育项目 2019HD206

详细信息
    通讯作者:

    唐小煜,讲师,Email:tangxy@scnu.edu.cn

  • 中图分类号: P407.8;TP75

Image Segmentation and Defect Detection of Insulators Based on U-net and YOLOv4

  • 摘要: 输电线路上绝缘子的完整性直接影响了输电的安全与可靠性.采用深度学习方法,对绝缘子图像识别提取和缺陷检测问题进行了研究.首先基于优化的U-net模型获取绝缘子区域掩模图像,实现对绝缘子串语义分割;然后基于YOLOv4模型获取缺陷绝缘子的位置,实现对自爆绝缘子目标的检测.为充分利用高分辨率图像的像素信息,提出“切分-识别-合成”的检测思路,精确分割出绝缘子以及判断并获取缺陷区域;最后设计了多组实验并进行对比,验证了模型的有效性.采用优化的U-net模型分割绝缘子的Dice系数达0.92;采用YOLOv4模型检测自爆绝缘子的识别精度达0.96,平均重叠度IOU达0.88.研究结果对实现电力系统运维的智能化具有较高的应用价值.
  • 图  1  U-net模型框架

    Figure  1.  The U-net model framework

    图  2  绝缘子串珠分割的流程图

    Figure  2.  The flow chart of semantic segmentation of the insulator

    图  3  不同数据集的分割结果

    Figure  3.  The segmentation results of different data sets

    图  4  YOLOv4框架

    Figure  4.  The YOLOv4 framework

    图  5  训练过程损失值和均值平均精度曲线

    Figure  5.  The loss and map curve of the training process

    图  6  绝缘子缺陷检测流程图

    Figure  6.  The flow chart of insulator defect detection

    图  7  滑动窗口切分示意图

    Figure  7.  The diagram of sliding window segmentation

    图  8  训练损失曲线与测试Dice系数曲线

    Figure  8.  The training loss curve and test Dice curve

    图  9  FCN模型与优化后U-net模型的分割效果对比

    Figure  9.  The comparison of segmentation results between the FCN model and the optimized U-net model

    图  10  优化后测试的效果图

    Figure  10.  The test results after optimization

    表  1  FCN和优化后U-net的效果对比

    Table  1.   The comparison of results between the FCN model and the optimized U-net model

    模型 图像数 精确率/% 召回率/% Dice/% 平均处理时间/ms
    FCN 126 91.83 81.16 85.56 202
    优化后U-net 126 94.34 89.05 91.31 191
    下载: 导出CSV

    表  2  检测优化前后的性能

    Table  2.   The performance before and after detection optimization

    模型 图像数 平均处理时间/ms 识别精确率/% 平均IOU
    优化的YOLOv4 304 53 0.96 0.88
    YOLOv4 304 53 0.88 0.76
    Faster-RCNN 304 605 0.93 0.83
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-22
  • 刊出日期:  2020-12-25

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