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3D defect detection of connectors based on structured light

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Abstract

In order to realize the rapid detection of three-dimensional defects of connectors, this paper proposes a method for detecting connector defects based on structured light. This method combines structured light with binocular stereo vision to obtain three-dimensional data for the connector. Point cloud registration is used to identify defects and decision trees are used to classify defects. The accuracy of the 3D reconstruction results in this paper is 0.01 mm, the registration accuracy of the point cloud reaches the sub-millimeter level, and the final defect classification accuracy is 94%. The experimental results prove the effectiveness of the proposed three-dimensional connector defect detection method in connector defect detection and classification.

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Authors and Affiliations

  1. Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tiangong University, Tianjin, 300387, China

    Yue Ji  (纪越), Yang Chen  (陈扬) & Li-mei Song  (宋丽梅)

  2. School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China

    Yan-gang Yang  (杨燕罡)

  3. Department of Precision Instrument, Tsinghua University, Beijing, 100084, China

    Huai-dong Yang  (杨怀栋)

Authors
  1. Yue Ji  (纪越)
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  2. Yang Chen  (陈扬)
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  3. Li-mei Song  (宋丽梅)
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  4. Yan-gang Yang  (杨燕罡)
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  5. Huai-dong Yang  (杨怀栋)
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Corresponding author

Correspondence to Li-mei Song  (宋丽梅).

Additional information

This work has been supported by the National Natural Science Foundation of China (Nos.61078041 and 51806150), the Natural Science Foundation of Tianjin (Nos.16JCYBJC15400, 15JCYBJC51700 and 18JCQNJC04400), the State Key Laboratory of Precision Measuring Technology and Instruments (Tianjin University) and the Program for Innovative Research Team in University of Tianjin (No.TD13-5036), and the Tianjin Enterprise Science and Technology Commissioner Project (No.18JCTPJC61700).

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Ji, Y., Chen, Y., Song, Lm. et al. 3D defect detection of connectors based on structured light. Optoelectron. Lett. 17, 107–111 (2021). https://doi.org/10.1007/s11801-021-9212-8

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  • DOI: https://doi.org/10.1007/s11801-021-9212-8

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