  Most of the existing object detection methods generate poor glass detection
results, due to the fact that the transparent glass shares the same appearance
with arbitrary objects behind it in an image. Different from traditional deep
learning-based wisdoms that simply use the object boundary as auxiliary
supervision, we exploit label decoupling to decompose the original labeled
ground-truth (GT) map into an interior-diffusion map and a boundary-diffusion
map. The GT map in collaboration with the two newly generated maps breaks the
imbalanced distribution of the object boundary, leading to improved glass
detection quality. We have three key contributions to solve the transparent
glass detection problem: (1) We propose a three-stream neural network (call
GlassNet for short) to fully absorb beneficial features in the three maps. (2)
We design a multi-scale interactive dilation module to explore a wider range of
contextual information. (3) We develop an attention-based boundary-aware
feature Mosaic module to integrate multi-modal information. Extensive
experiments on the benchmark dataset exhibit clear improvements of our method
over SOTAs, in terms of both the overall glass detection accuracy and boundary
clearness.
