面向列车轮轨接触区域图像分割的生成对抗网络*
DOI:
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金(61763023)项目资助


Generative adversarial network for image segmentation of train wheelrail contact area
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    列车和轨道之间的开放约束条件决定了车辆脱轨的客观存在。轮轨接触区域边缘曲线分割对列车轮轨接触关系的研究具有重要意义,提出了一种基于生成对抗网络的轮轨接触区域边缘曲线分割算法。通过将残差模块引入生成器网络中,增强了网络对输出变化的敏感程度,进而更好的调整生成器权重。此外,膨胀残差模块的引入,有效扩大了特征图的接收区域。实验结果显示,改进的生成对抗网络对轮轨接触区域边缘曲线的分割准确度达到9613%,敏感度、特异度、F1值、ROC曲线下的面积分别为8390%、9713%、8367%和9812%,验证了该方法能够准确分割轮轨接触区域边缘曲线。

    Abstract:

    The open constraint condition between the train and the track determines the objective existence of the train derailment. Curve segmentation of the edge of wheelrail contact area is of great significance to the research of the train wheelrail contact relationship. In this paper, an algorithm for the curve segmentation of the edge of wheelrail contact area based on generative adversarial networks is proposed. By introducing the residual module into the generator network, the sensitivity of the network to output changes is enhanced, and the generator weight can be better adjusted. In addition, in order to effectively expand the receiving area of the feature map, the expansion residual module is introduced. The experimental results show that the accuracy of curve segmentation of the edge of wheelrail contact area reaches 9613% by improved generative adversarial networks, and the sensitivity, specificity, F1 value and area under the ROC curve is 8390%, 9713%, 8367% and 9812% respectively, which verify that this method can accurately segment the edge curve of the wheelrail contact area.

    参考文献
    相似文献
    引证文献
引用本文

秦菲菲,董昱.面向列车轮轨接触区域图像分割的生成对抗网络*[J].电子测量与仪器学报,2021,35(11):154-162

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-06-08
  • 出版日期:
文章二维码