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Most of the existing object detection methods generate poor glass detectionresults, due to the fact that the transparent glass shares the same appearancewith arbitrary objects behind it in an image. Different from traditional deeplearning-based wisdoms that simply use the object boundary as auxiliarysupervision, we exploit label decoupling to decompose the original labeledground-truth (GT) map into an interior-diffusion map and a boundary-diffusionmap. The GT map in collaboration with the two newly generated maps breaks theimbalanced distribution of the object boundary, leading to improved glassdetection quality. We have three key contributions to solve the transparentglass detection problem: (1) We propose a three-stream neural network (callGlassNet 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 ofcontextual information. (3) We develop an attention-based boundary-awarefeature Mosaic module to integrate multi-modal information. Extensiveexperiments on the benchmark dataset exhibit clear improvements of our methodover SOTAs, in terms of both the overall glass detection accuracy and boundaryclearness.

GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection