397 / 2021-11-26 16:27:40
Research on Deep Learning-based Deraining Method of Catenary Images
Catenary; Deep learning; Supervised attention module; Image deraining; Component positioning
Draft Accepted
Weiping Guo / Southwest Jiaotong University
Hui Wang / Southwest Jiaotong University
Lina Mao / Beijing Jiaotong University
Zhiwei Han / Southwest Jiaotong University
Zhigang Liu / School of Electrical Engineering; Southwest Jiaotong University
In heavy rain, the catenary images collected by railway detection devices have a severe noise problem. The rain streaks in the image significantly affect the image quality, decreasing the accuracy and efficiency of the automatic identification of catenary components. Therefore, this paper proposes a deep learning-based deraining method of blurry catenary images under heavy rain to solve the problem. This method adopts a two-stage architecture, consisting of the encoder-decoder structure and single-scale convolution, respectively. And a supervised attention module is added to every stage to improve feature transmission efficiency. The experiment results prove that our method can effectively improve the accuracy of component positioning.
Important Date
  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST