@inproceedings{c4fcad21c4b9442c9a9b96e08232b668,
title = "Shadow Removal using GTA Road Dataset",
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. ",
keywords = "Deep Neural Network, Shadow Detection, Shadow Removal",
author = "Geon Kang and Woojin Ahn and Hyunduck Choi and Myotaeg Lim",
note = "Publisher Copyright: {\textcopyright} 2021 ICROS.; 21st International Conference on Control, Automation and Systems, ICCAS 2021 ; Conference date: 12-10-2021 Through 15-10-2021",
year = "2021",
doi = "10.23919/ICCAS52745.2021.9649812",
language = "English",
series = "International Conference on Control, Automation and Systems",
publisher = "IEEE Computer Society",
pages = "2203--2205",
booktitle = "2021 21st International Conference on Control, Automation and Systems, ICCAS 2021",
}