IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020

Don't Hit Me! Glass Detection in Real-world Scenes

Haiyang Mei1     Xin Yang1,4,*     Yang Wang1     Yuanyuan Liu1     Shengfeng He2    
Qiang Zhang1     Xiaopeng Wei1,*     Rynson W.H. Lau3

1 Dalian University of Technology     2 South China University of Technology    
3 City University of Hong Kong     4 Advanced Institute of Information Technology Peking University

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Glass is very common in our daily life. Existing computer vision systems neglect the glass and thus might lead to severe consequence, e.g., the robot might crash into the glass wall. However, sensing the presence of the glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass and the content presented in the glass region typically similar to those outside of it. In this paper, we raise an interesting but 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, by learning abundant contextual features from a global perspective with a novel large-field contextual feature integration module. Extensive experiments demonstrate the proposed method achieves superior glass detection results on our GDD test set. Particularly, we outperform state-of-the-art methods that fine-tuned for glass detection.


Visual Results


Paper : [ GDNet.pdf ]
Experimental results : [ ]
Pre-trained model. : [ GDNet.pth ]
Source Code. : [ Code ]


Both training set and testing set can be obtained via e-mail request!

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The dataset can be used freely if you agree with all the following terms.
- The dataset is used only for non-commercial purposes, such as teaching and research. You do not use the dataset or any of its modified versions for any purposes of commercial advantage or private financial gain.
- You do not distribute the dataset or any of its modified versions to other individuals, institutes, companies, associations or public.
- In case you use the dataset within your research papers, you refer to our publications on our website. If the dataset is used in media, a link to our website is included.
- We reserve all rights that are not explicitly granted to you. The dataset is provided as is, and you take full responsibility for any risk of using it. There may be inaccuracies although we tried, and will try our best to rectify any inaccuracy once found.


    author = {Mei, Haiyang and Yang, Xin and Wang, Yang and Liu, Yuanyuan and He, Shengfeng and Zhang, Qiang and Wei, Xiaopeng and Lau, Rynson W.H.},
    title = {Don't Hit Me! Glass Detection in Real-World Scenes},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}

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