Skip to main content
Springer Nature Link
Log in

Real-time recognition method for PCB chip targets based on YOLO-GSG

  • Research
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In modern industrial settings, the identification of chips on PCB boards is crucial for quality control and efficiency. However, achieving both speed and accuracy in chip detection remains a significant challenge. To address this issue, we propose the YOLO-GSG deep network model, which incorporates several novel modifications to the standard YOLO architecture. The key innovations include the replacement of the ELAN module with the C3Ghostnet module in the backbone network, improving feature extraction and reducing model complexity, and the introduction of the SE attention mechanism to minimize feature loss. Additionally, the GSnet module and GSConv convolution are integrated into the neck network to enhance feature fusion. The experimental results indicate that the YOLO-GSG algorithm achieves a mAP of 99.014%, with precision and recall improvements of 1.080% and 1.446% over the baseline YOLOv7 model. Additionally, the improved model has 24.478M parameters, 61.4 GFLOPs, and a model size of 50.8 MB. The model achieves an FPS of 231.55, representing a 12.8% speedup over the baseline. These results indicate that the YOLO-GSG model offers a superior balance of speed and accuracy for chip identification in industrial applications. This study contributes to the advancement of deep learning applications in industrial environments, providing a more efficient and effective tool for quality control in PCB manufacturing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Zheng, L.J., Zhang, X., Wang, C., Wang, L., Li, S., Song, Y.X., Zhang, L.: Experimental study of micro-holes position accuracy on drilling flexible printed circuit board. In: 11th Global Conference on Sustainable Manufacturing, Berlin, Germany. 268–272 (2013). https://doi.org/10.14279/DEPOSITONCE-4784

  2. Ke, Y.J.: Research on PCB component positioning and identification technology. Nanjing University of Science and Technology, M.A (2017)

    MATH  Google Scholar 

  3. Fan, C.H.: Implementation of printed IC chip character recognition system based on heterogeneous platform. Shanghai University, M.A (2022)

    MATH  Google Scholar 

  4. Kerdvibulvech, C.: Human hand motion recognition using an extended particle filter. In: Perales, F.J., Santos-Victor, J. (eds) Articulated Motion and Deformable Objects. AMDO 2014. Lecture Notes in Computer Science, vol 8563. Springer, Cham. https://doi.org/10.1007/978-3-319-08849-5_8

  5. Kerdvibulvech, C.: Hand tracking by extending distance transform and hand model in real-time. Pattern Recognit Image Anal. 25, 437–441 (2015). https://doi.org/10.1134/S1054661815030098

    Article  MATH  Google Scholar 

  6. Zhang, L., Zhang, S.H., Yan, S., Yuan, Y.: Deep learning-based object detection and robotic arm grasping. J Xian Polytech Univ. 38(4), 65–72 (2024). https://doi.org/10.13338/j.issn.1674-649x.2024.04.009

    Article  MATH  Google Scholar 

  7. Wang, Y., Yang, G., Guo, J.: Vehicle detection in surveillance videos based on YOLOv5 lightweight network. Bull. Polish Acad Sci. Tech. Sci.. 70(6), 43644 (2023). https://doi.org/10.24425/bpasts.2022.143644

    Article  Google Scholar 

  8. Zhang, J.Y., Jing, J.F.: Defect classification of chopped strand mats based on deep learning and piecewise linear interpolation. J Xi’an Polytech Univ. 32(5), 553–559 (2018). https://doi.org/10.13338/j.issn.1674-649x.2018.05.011

    Article  MathSciNet  MATH  Google Scholar 

  9. Ding, R., Dai, L., Li, G., et al.: TDD-Net: a tiny defect detection network for printed circuit boards. CAAI Trans. Intell. Technol. 4, 110–116 (2019). https://doi.org/10.1049/trit.2019.0019

    Article  MATH  Google Scholar 

  10. Wu, L., Zhang, L., Zhou, Q.: Printed circuit board quality detection method integrating lightweight network and dual attention mechanism. IEEE Access. 10, 87617–87629 (2022). https://doi.org/10.1109/ACCESS.2022.3198994

    Article  Google Scholar 

  11. Zheng, J., Sun, X., Zhou, H., Tian, C., Qiang, H.: Printed circuit boards defect detection method based on improved fully convolutional networks. IEEE Access. 10, 109908–109918 (2022). https://doi.org/10.1109/ACCESS.2022.3214306

    Article  Google Scholar 

  12. Yu, Z., Wu, Y., Wei, B., et al.: A lightweight and efficient model for surface tiny defect detection. Appl. Intell. 53, 6344–6353 (2023). https://doi.org/10.1007/s10489-022-03633-x

    Article  MATH  Google Scholar 

  13. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada. pp. 7464–7475 (2023). https://doi.org/10.1109/CVPR52729.2023.00721

  14. Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). pp. 2778–2788 (2021). https://doi.org/10.1109/ICCVW54120.2021.00312

  15. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745

  16. Ding, X., Zhang, X., Ma, N., Han, J., et al.: RepVGG: Making VGG-style ConvNets Great Again. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13728–13737(2021). https://doi.org/10.1109/CVPR46437.2021.01352

  17. Liang, Y.Q.: Research on edge learning based visual inspection system - character recognition and defect detection as an example. Liaocheng University, M.A (2023)

    MATH  Google Scholar 

  18. She, Y.T.: Single crystal silicon wire detection system based on Yolo algorithm. Lanzhou University, M.A (2024)

    MATH  Google Scholar 

  19. Zhang, X., Zhou, X., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 6848–6856, https://doi.org/10.1109/CVPR.2018.00716

  20. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. https://doi.org/10.48550/arXiv.1704.04861

  21. K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu and C. Xu,: GhostNet: more features from cheap operations, In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 1577–1586, https://doi.org/10.1109/CVPR42600.2020.00165

  22. Li, X., Yun, X., Zhao, Z., Zhang, K., Wang, W.: Lightweight deeplearning method for multi-vehicle object recognition. Inf. Technol. Control. 51, 294–312 (2022). https://doi.org/10.5755/j01.itc.51.2.30667

    Article  MATH  Google Scholar 

  23. Jin, L., Yu, Y., Zhou, J., Bai, D., et al.: SWVR: a lightweight deep learning algorithm for forest fire detection and recognition. Forests 15(1), 204 (2024). https://doi.org/10.3390/f15010204

    Article  MATH  Google Scholar 

  24. Hu, J., Wang, Z., Chang, M., Xie, L., Xu, W., Chen, N.: PSG-Yolov5: a paradigm for traffic sign detection and recognition algorithm based on deep learning. Symmetry. 14(11), 2262 (2022). https://doi.org/10.3390/sym14112262

    Article  MATH  Google Scholar 

  25. Su, T., Wang, Y., Deng, QY., et al.: Improved pedestrian and vehicle detection algorithm in foggy weather based on YOLOv5. J Syst Simul. pp 1–11 (2024)

  26. Li, M., Chen, S., Sun, C., Fang, S., Han, J., et al.: An improved lightweight dense pedestrian detection algorithm. Appl. Sci. 13(15), 8757 (2023). https://doi.org/10.3390/app13158757

    Article  MATH  Google Scholar 

  27. Tang, Y., Han, K., Guo, JY., et al.: GhostNetV2: enhance cheap operation with long-range attention. https://doi.org/10.48550/arXiv.2211.12905

  28. P. K. A. Vasu, J. Gabriel, J. Zhu, O. Tuzel and A. Ranjan, : MobileOne: An Improved One millisecond Mobile Backbone.: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 7907–7917, https://doi.org/10.1109/CVPR52729.2023.00764

  29. Ma, N., Zhang, X., Zheng, HT., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11218. Springer, Cham. https://doi.org/10.1007/978-3-030-01264-9_8

  30. Howard, A. et al.: Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 1314–1324, https://doi.org/10.1109/ICCV.2019.00140

  31. Woo, S., Park, J., Lee, JY., Kweon, I.S.: CBAM: convolutional block attention module. In: Computer Vision – ECCV 2018:15th European Conference. vol. 11211 (2018). https://doi.org/10.1007/978-3-030-01234-2_1

  32. Yang, L., Zhang, R., Li, L., Xie, X.: SimAM: a simple, parameter-free attention module for convolutional neural networks. Int. Conf. Mach. Learn.. 139, 11863–11874 (2021)

    MATH  Google Scholar 

  33. Wang, Q., Wu, B., Zhu, P., Li, P., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11531–11539 (2020). https://doi.org/10.1109/CVPR42600.2020.01155

  34. Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1489–1500 (2023). https://doi.org/10.1109/TPAMI.2022.3164083

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61971339), Basic Research Program of Natural Science of Shaanxi province (No.2022JM407), Innovation and Entrepreneurship training program for university students (No.S202310709041).

Author information

Authors and Affiliations

  1. School of Electronics and Information, Xi’an Polytechnic University, 58 Shangu Road, Lintong District, Xi’an City, Shaanxi Province, China

    Zeang Yue, Xun Li, Huilong Zhou & Wenjie Wang

  2. School of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Aksu, 843100, China

    Gaopin Wang

Authors
  1. Zeang Yue
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xun Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Huilong Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Gaopin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wenjie Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Zeang Yue wrote the main text of the manuscript and carried out specific experimental studies, Xun Li provided theoretical guidance, revised the paper, and provided experimental equipment, Huilong Zhou participated in some of the experiments and reviewed the article, Gaopin Wang and Wenjie Wang provided ideas and theoretical guidance for the paper, and all of them reviewed the first draft.

Corresponding author

Correspondence to Xun Li.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yue, Z., Li, X., Zhou, H. et al. Real-time recognition method for PCB chip targets based on YOLO-GSG. J Real-Time Image Proc 22, 44 (2025). https://doi.org/10.1007/s11554-024-01616-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-024-01616-4

Keywords