结合边缘感知与形状感知深度网络的织物叠堆顶层织物精确分割
Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks
浏览论文内容
中文总结 AI 辅助
针对织物叠堆顶层分割的复杂挑战，提出结合边缘感知与形状感知分支的编码器-解码器训练架构，在真实数据集上验证其性能优于现有基准。
中文摘要 AI 辅助
织物叠堆操作需要对最顶层织物层进行精确分割，该任务因织物边界细微、各层织物视觉相似度高而变得复杂。现有的语义分割及基于边缘的分割方法往往难以应对这些复杂性，限制了不同任务下机器人操控的性能。本研究提出一种针对叠堆织物顶层分割的新型分割训练架构，该方法在经典编码器-解码器框架基础上引入两个专用分支：边缘感知分支与形状感知分支，用于监督骨干网络以实现更好的调优。边缘感知分支增强边界描绘，形状感知分支则引导网络捕获并对齐整体织物形状，使其与计算机辅助设计（CAD）模型生成的参考掩码保持一致。在真实织物数据集上开展的实验表明，该训练方法优于现有基准方法，通过定量结果与 ablation 研究验证了多分支设计的有效性。
英文摘要
Fabric destacking requires precise segmentation of the topmost fabric layer, a task complicated by subtle fabric boundaries and high visual similarity between fabric layers. Existing semantic and edge-based segmentation approaches often struggle with these complexities, limiting the performance of robotic manipulation for different tasks. In this work, a novel segmentation training architecture tailored for top-layer fabric segmentation in stacked fabrics is proposed. The method extends the classical encoder-decoder framework by introducing two specialized branches - an edge-aware branch and a shape-aware branch - that are used to supervise the backbone network for better tuning. The edge-aware branch enhances boundary delineation, while the shape-aware branch guides the network to capture and align the overall fabric shape with reference masks derived from Computer Aided Design (CAD) models. Experiments on a real-world fabric dataset demonstrate that the training approach outperforms established baselines, verifying the effectiveness of the multi-branch design through both quantitative results and ablation studies.
