In the field of curtain wall construction, manual installation presents significant safety hazards and suffers from low efficiency, while automated installation is constrained by the limited localization capabilities of curtain wall installation robots. In this paper, an automated installation solution based on machine vision is proposed, and a detailed discussion of several steps involved is provided. To locate the installation area, DANF, a deep learning-based dual-flow aggregation network designed for curtain wall frame segmentation, is proposed. It employs Transformer for global analysis and CNNs for detailed feature extraction to handle curtain wall frame structures. On the dataset constructed in this paper, DANF achieves an IoU of 85.19 % with a parameter count of only 4.24 M, demonstrating higher accuracy compared to other algorithms. Additionally, a pose-solving method based on the semantic segmentation results of the curtain wall frame is designed to adapt to curtain wall installation scenarios.

中文翻译：

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基于双流聚合网络的幕墙框架分割：在机器人姿态估计中的应用


在幕墙施工领域，人工安装存在重大的安全隐患，效率低下，而自动化安装受到幕墙安装机器人定位能力有限的制约。本文提出了一种基于机器视觉的自动化安装方案，并详细讨论了所涉及的几个步骤。针对安装区域的定位，提出了一种基于深度学习的双流聚合网络 DANF，该网络专为幕墙框架分割而设计。它使用 Transformer 进行全局分析，并使用 CNN 进行详细特征提取，以处理幕墙框架结构。在本文构建的数据集上，DANF 实现了 85.19% 的 IoU，参数计数仅为 4.24 M，与其他算法相比，表现出更高的准确性。此外，针对幕墙安装场景，设计了一种基于幕墙框架语义分割结果的位姿解算方法。