Large-Field Contextual Feature Learning for Glass Detection

Authors：Mei, Haiyang; Yang, Xin^*; Yu, Letian; Zhang, Qiang; Wei, Xiaopeng^*; Lau, Rynson W. H.^*

Source：IEEE Transactions on Pattern Analysis and  Machine Intelligence, 2023, 45(3): 3329-3346.

DOI：10.1109/TPAMI.2022.3181973

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

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.