  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.
In this paper, we propose an important problem of detecting glass surfaces from
a single RGB image. To address this problem, we construct the first large-scale
glass detection dataset (GDD) and propose a novel glass detection network,
called GDNet-B, which explores abundant contextual cues in a large
field-of-view via a novel large-field contextual feature integration (LCFI)
module and integrates both high-level and low-level boundary features with a
boundary feature enhancement (BFE) module. Extensive experiments demonstrate
that our GDNet-B achieves satisfying glass detection results on the images
within and beyond the GDD testing set. We further validate the effectiveness
and generalization capability of our proposed GDNet-B by applying it to other
vision tasks, including mirror segmentation and salient object detection.
Finally, we show the potential applications of glass detection and discuss
possible future research directions.
