Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks
This work addresses the challenge of segmenting the topmost layer in stacked fabrics, where ambiguous boundaries and high inter-layer visual similarity hinder accurate delineation. To tackle this issue, the authors propose an edge-aware and shape-aware dual-branch encoder-decoder architecture. The method innovatively integrates an edge detection branch with CAD model–derived shape priors, leveraging a dual-branch supervision mechanism during joint training to simultaneously enhance boundary precision and global shape alignment. Experimental results on a real-world fabric dataset demonstrate that the proposed approach significantly outperforms existing baselines, while ablation studies confirm the effectiveness of the multi-branch design in improving segmentation performance.