MCFR：一种用于鲁棒多型号板对板连接器组装的掩模引导的由粗到精回归框架

发表机构

Tsinghua University（清华大学）

AI 中文总结

针对多型号板对板连接器自动组装中视觉精度与部署鲁棒性不足的问题，提出掩模引导的由粗到精回归框架MCFR，通过掩模先验与光度精化抑制背景干扰，在真实任务中达到99.25%插入成功率。

AI 中文摘要

在3C制造中，板对板（BTB）连接器的自动插入需要高视觉精度和强部署鲁棒性。该问题仍具挑战性，因为多型号连接器表现出显著的形态和外观差异，使得稳定的跨型号泛化变得困难，同时固定视角检测设置下训练与部署之间的不匹配会引起背景虚假相关，并降低实际性能。为解决这些问题，本文提出MCFR，一种用于多型号BTB连接器组装的掩模引导的由粗到精回归框架。通过引入对象感知掩模先验和显式光度精化，所提方法抑制了背景干扰，并提高了实际部署中的对齐精度和鲁棒性。在自建多型号数据集、BTB批量插入测试平台和真实智能手机组装任务上的实验表明，MCFR持续优于代表性基线，并实现了99.25%的平均实际插入成功率。这些结果证明了MCFR在多型号BTB连接器自动组装中的有效性和实际潜力。

英文摘要

Automated insertion of board-to-board (BTB) connectors in 3C manufacturing requires both high visual accuracy and strong deployment robustness. This problem remains challenging because multi-variant connectors exhibit significant morphological and appearance variations, making stable cross-variant generalization difficult, while the mismatch between training and deployment under fixed-view inspection settings induces background spurious correlation and degrades real-world performance. To address these issues, this paper proposes MCFR, a Mask-Guided Coarse-to-Fine Regression framework for multi-variant BTB connector assembly. By introducing an object-aware mask prior and explicit photometric refinement, the proposed method suppresses background interference and improves alignment accuracy and robustness in practical deployment. Experiments on a self-constructed multi-variant dataset, a BTB batch insertion testbed, and a real smartphone assembly task show that MCFR consistently outperforms representative baselines and achieves an average real-world insertion success rate of 99.25%. These results demonstrate the effectiveness and practical potential of MCFR for automated assembly of multi-variant BTB connectors.

论文原文

arXiv 摘要页 · PDF 原文 · HTML 原文

相关深度报道

• MCFR：用于多型号手机连接器装配的粗到精回归框架