Abstract
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.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Shen, G., Wang, S., & Wu, D. (2026). MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly. https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly
MLA 9
Shen, Guanghui, et al. "MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly." https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly.
Chicago (author–date)
Shen, Guanghui, Song Wang, and Dan Wu. 2026. "MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly." https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly.
Harvard
Shen, G., Wang, S. and Wu, D. (2026) 'MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly', Available at: https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly.
Vancouver
Shen G, Wang S, Wu D. MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly. https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly
IEEE
G. Shen, S. Wang, and D. Wu, "MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly," https://omanscience.com/en/articles/mcfr-a-mask-guided-coarse-to-fine-regression-framework-for-robust-multi-variant-board-to-board-connector-assembly.