JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v9i6.13160ArticlePanoramic Glass Image Segmentation Networkhttps://artdesignp.com/journal/JERA/9/6/10.26689/jera.v9i6.13160PanGuanlin,CuiYan,ChangQingling,LiKelin,OuYangtao,YuHaohui2025962025-12-16In panoramic images, the geometric distortion caused by wide-angle lenses makes traditional semantic segmentation methods difficult to accurately segment the glass areas. To address the challenges of capturing spatial features and integrating context information, we propose the Panoramic Glass Image Segmentation Network (PGISNet). This network integrates the Matrix Decomposition Base Module (MDBM), the Transparent Perception Consistency Module (TACM), the Context and Texture Compensation Module (CTCM), and the Multi-scale Gated Context Attention Module (MGCA), constructing a progressive feature processing flow. Experimental results on the PanoGlassV2 benchmark test show that PGISNet achieved 90.03% IoU, 94.76% F-score, and 94.0% PA, significantly outperforming existing methods, verifying its effectiveness and advancement in the panoramic image glass segmentation task.

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