Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2020
Abstract
Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass, and the content within the glass region is typically similar to those behind it. In this paper, we propose an important problem of detecting glass from a single RGB image. To address this problem, we construct a large-scale glass detection dataset (GDD) and design a glass detection network, called GDNet, which explores abundant contextual cues for robust glass detection with a novel large-field contextual feature integration (LCFI) module. Extensive experiments demonstrate that the proposed method achieves more superior glass detection results on our GDD test set than state-of-the-art methods fine-tuned for glass detection.
Discipline
Graphics and Human Computer Interfaces
Research Areas
Information Systems and Management
Publication
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, Online, June 14-19
First Page
3687
Last Page
3696
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
MEI, Haiyang; YANG, Xin; WANG, Yang; LIU, Yuanyuan; HE, Shengfeng; ZHANG, Qiang; WEI, Xiaopeng; and LAU, Rynson W.H..
Don't hit me! glass detection in real-world scenes. (2020). Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, Online, June 14-19. 3687-3696.
Available at: https://ink.library.smu.edu.sg/sis_research/8524
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.