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 language | English |
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| Title of host publication | 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021 |
| Publisher | IEEE Computer Society |
| Pages | 2203-2205 |
| Number of pages | 3 |
| ISBN (Electronic) | 9788993215212 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of Duration: 2021 Oct 12 → 2021 Oct 15 |
Publication series
| Name | International Conference on Control, Automation and Systems |
|---|---|
| Volume | 2021-October |
| ISSN (Print) | 1598-7833 |
Conference
| Conference | 21st International Conference on Control, Automation and Systems, ICCAS 2021 |
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| Country/Territory | Korea, Republic of |
| City | Jeju |
| Period | 21/10/12 → 21/10/15 |
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