Abstract
To address the issue of misjudgment in traditional connected domain marking algorithms during the dense assembly and welding of SMT components, a method combining connected domain marking with localized watershed algorithms has been proposed. After preprocessing the component images, including grayscale conversion and noise filtering, the connected domain marking algorithm’s identified soldered areas are masked and processed. The soldered areas are then extracted, histogram equalization and distance transformation are performed, and the watershed algorithm is used to segment the misjudged soldering areas. An SMT solder defect detection test bench was developed on the LabVIEW platform, and a comparative experiment was conducted with traditional connected domain algorithms. The experimental results demonstrate that the optimized algorithm not only maintains the accuracy of traditional connected-component labeling but also significantly reduces the false-positive rate in densely soldered environments. Consequently, it exhibits stronger adaptability and robustness to interference, better fulfilling the requirements of real-world engineering applications. This method exhibits superior environmental adaptability and markedly higher interference resistance, rendering it well-suited to real-world engineering applications.
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All data generated or analysed during this study are included in this published article.
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Acknowledgements
We declare that no part of the manuscript (text, figures, data analysis or raw data) was entirely generated or altered by an AI tool. The output was read sentence-by-sentence by all co-authors (native and non-native speakers) and cross-checked against the original Chinese draft. DeepSeek was used only to brainstorm the flow of the Discussion section. The suggestions were evaluated against the cited references and 40% of them were discarded; the remaining points were re-phrased by the authors and referenced. No AI tool had access to the raw data or was used for statistical analysis, figure preparation, or literature screening. All authors take full responsibility for the final content.
Funding
The authors gratefully acknowledge the financial support from the Henan Province’s new round of key disciplines-Mechanical Engineering, Zhengzhou local university’s fourth batch of technical and skilled master studios-Mechatronic Engineering (Zheng Jiao Gao Han [2023] No.864) and National College Students’ Innovation Training Program project (202411834024).
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Wei Xiong: Writing – original draft, Investigation, Funding acqui-ition. Na Xiao: Methodology, Inves-tigation, Data curation. Ruili Wang: Writing – review & editing, Funding acquisition, Conceptualization.
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Xiong, W., Xiao, N. & Wang, R. A SMT pin soldering defect detection system based on improved connectivity domain algorithm. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44847-0
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DOI: https://doi.org/10.1038/s41598-026-44847-0
