Large-Field Contextual Feature Learning for Glass Detection

• URL: http://arxiv.org/abs/2209.04639v1
• Date: Sat, 10 Sep 2022 11:08:05 GMT
• Title: Large-Field Contextual Feature Learning for Glass Detection
• Authors: Haiyang Mei, Xin Yang, Letian Yu, Qiang Zhang, Xiaopeng Wei, Rynson W.H. Lau
• Abstract summary: 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) We propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view.
• Score: 44.222075782263175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

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