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

TL;DR

GlassNet introduces a three-stream neural network that leverages label decoupling into interior and boundary maps, significantly improving the accuracy and boundary clarity in transparent glass detection.

Contribution

The paper presents a novel label decoupling approach and a three-stream neural network architecture specifically designed for robust glass detection.

Findings

Outperforms state-of-the-art methods in accuracy.

Enhances boundary detection clarity.

Effective multi-scale contextual exploration.

Abstract

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.