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The enhancement of catenary image with low visibility based on multi-feature fusion network in railway industry

机译：基于多特征融合网络的铁路网低可视度接触网图像增强

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摘要

In the Industrial Internet of Things (IIoT), the security and efficiency are indispensable. For the railway industry, the video from inspection vehicle would be influenced by various factors with low visibility and hard for high level vision task, such as the fault diagnosis of catenary system. In this paper, we propose a method based on the multi-feature fusion network to improve the quality and visual effect of the catenary images. The transmission map is learned from the multi-scale and multi-feature fusion network, which would learn coarse and fine details and combine the latent features. In the catenary image, the sky and non-sky regions are segmented through multiple accommodative thresholds to estimate the atmospheric light value. With the refinement of transmission map, the restored catenary image is obtained through the atmospheric scattering model. In the experimental results, it can be seen that the proposed method can improve the clarity of catenary image in haze. The quantitative evaluation shows that it has better visual effect compared with the other traditional methods.
机译：在工业物联网（IIoT）中，安全性和效率是必不可少的。对于铁路行业而言，来自视察车辆的视频会受到可见度低，难以实现高水平视觉任务的各种因素的影响，例如悬链系统的故障诊断。本文提出了一种基于多特征融合网络的方法，以提高接触网图像的质量和视觉效果。传输图是从多尺度和多特征融合网络中学习的，该网络将学习粗略和精细的细节并结合潜在特征。在悬链线图像中，天空区域和非天空区域通过多个适应性阈值进行分割，以估算大气光值。随着传输图的细化，通过大气散射模型获得了恢复的悬链线图像。在实验结果中，可以看出，该方法可以提高雾霾中悬链线图像的清晰度。定量评估表明，与其他传统方法相比，它具有更好的视觉效果。

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  • 来源
    《Computer Communications》 |2020年第2期|200-205|共6页
  • 作者

  • 作者单位

    Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China;

    Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA;

    Gonzaga Univ Spokane WA 99258 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Catenary system image; Low visibility; Deep learning;

    机译：接触网系统图像;能见度低;深度学习;

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