Global Feature Polishing Network for Glass-Like Object Detection.

Glass object detection aims to detect and segment glass-like objects in an input image. Compared with other binary segmentation tasks, the transparent property of glass brings great challenges to glass object detection. This requires the model to capture more richer image global semantics and local detail information. In this work, we propose a novel global feature polishing network for glass object detection. We first design a global perception module for coarse localization by embedding a self-attention block on top of the backbone. Then we propose a global feature polishing module to establish long-distance semantic dependence between different pixels and a multi-scale refinement module to combine multi-level side-output features, which can well explore the missing object parts and also refine the false detection in previous layers. In addition, we build a challenging Window dataset for comprehensive evaluation and further research. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art methods without any pre-processing and post-processing.