Large-Field Contextual Feature Learning for Glass Detection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations


  • Haiyang Mei
  • Xin Yang
  • Letian Yu
  • Qiang Zhang
  • Xiaopeng Wei

Related Research Unit(s)


Original languageEnglish
Number of pages17
Journal / PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
Online published19 Aug 2022
Publication statusOnline published - 19 Aug 2022


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.

Research Area(s)

  • boundary cue, Feature extraction, Glass, Glass detection, Image segmentation, large-field contextual features, Mirrors, Object detection, Reflection, Task analysis, transparent surface