Shadow Removal using GTA Road Dataset

Geon Kang, Woojin Ahn, Hyunduck Choi, Myotaeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a end-to-end Road Shadow Removal Network (RSRNet) on GTA road scene. Proposed network consists of shadow detection part and removal part. The shadow detection network separately predicts edges and regions of the shadow to accurately predict shadow masks. Given the mask, the shadow removal network removes shadows by predicting parameters of the shadow region between shadow free and shadow. The RSR network effectively removes the shadow while preserving the non-shadow region information. We evaluate proposed network quantitatively and qualitatively to confirm the performance on shadow removal in complex scenes.

Original languageEnglish
Title of host publication2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PublisherIEEE Computer Society
Pages2203-2205
Number of pages3
ISBN (Electronic)9788993215212
DOIs
Publication statusPublished - 2021
Event21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of
Duration: 2021 Oct 122021 Oct 15

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2021-October
ISSN (Print)1598-7833

Conference

Conference21st International Conference on Control, Automation and Systems, ICCAS 2021
Country/TerritoryKorea, Republic of
CityJeju
Period21/10/1221/10/15

Bibliographical note

Publisher Copyright:
© 2021 ICROS.

Keywords

  • Deep Neural Network
  • Shadow Detection
  • Shadow Removal

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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