SpringerLink
Log in

Defect Detection of Exposure Lead Frame Based on Improved YOLOX

  • Conference paper
  • First Online:
Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 153))

  • 1602 Accesses

Abstract

In view of the unpredictable defects in the industrial lead frame production line, many factories invest a lot of manpower resources and material re-sources to participate in the inspection process, which will cause huge losses to the producers, and the human eyes have the problems such as easy fatigue and low efficiency. In this paper, for the research on defect detection in lead frame exposure production line, the Efficient Channel Attention module is added to YOLOX algorithm to optimize the backbone network to improve the feature extraction capability and detection efficiency of the algorithm, and Varifocal Loss is introduced to solve the problem of very few defect samples in industry. After a lot of experiments, it has been proved that the evaluation indicator mAP@0.5 of the model trained by the improved YOLOX algorithm is increased by 2.10%, which is lightweight and has a higher accuracy.

This work is supported by Science Research Plan of Shaanxi Provincial Department of Education under Grant No. 19JC036.

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

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 234.33
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 299.59
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Shumin, D., Zhoufeng, L., Chunlei, L.: AdaBoost learning for fabric defect detection based on HOG and SVM. In: International Conference on Multimedia Technology. pp. 2903–2906 (2011)

    Google Scholar 

  2. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  3. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2117–2125 (2017)

    Google Scholar 

  4. Karunakaran, V.: Deep learning based object detection using mask RCNN. In: International Conference on Communication and Electronics Systems (ICCES). pp. 1684–1690(2021)

    Google Scholar 

  5. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, 29 (2016)

    Google Scholar 

  6. Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J.: YOLOX: Exceeding YOLO series in 2021. ar**v preprint ar**v: 2107.08430 (2021)

    Google Scholar 

  7. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A.C.: SSD: Single shot multibox detector. In: European Conference on Computer Vision. Springer, Cham. pp. 21–37 (2016)

    Google Scholar 

  8. Li, C., Cai, J., Qiu, S., Liang, H.: Defects detection of steel based on YOLOv4. In: China Automation Congress (CAC). IEEE, pp. 5836–5839 (2021)

    Google Scholar 

  9. Li, W., Dong, T., Shi, H., Ye, L.: Defect detection algorithm of wire rope based on color segmentation and Faster RCNN. In: 2021 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, pp. 656–661 (2021)

    Google Scholar 

  10. Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  11. Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: VarifocalNet: An IoU-aware dense object detector. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 8514–8523 (2021)

    Google Scholar 

  12. Wu, S., Li, X., Wang, X.: IoU-aware single-stage object detector for accurate localization. Image Vis. Comput. 97, 103911 (2020)

    Article  Google Scholar 

  13. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132–7141 (2018)

    Google Scholar 

  14. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2980–2988 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. **’an University of Posts and Telecommunications, **’an 710121, China

    Wanyu Deng, Dunhai Wu, Jiahao Jie & Wei Wang

Authors
  1. Wanyu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dunhai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiahao Jie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dunhai Wu .

Editor information

Editors and Affiliations

  1. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning **ong

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK

    Maozhen Li

  3. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Kenli Li

  4. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Zheng **ao

  5. College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China

    Longlong Liao

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, W., Wu, D., Jie, J., Wang, W. (2023). Defect Detection of Exposure Lead Frame Based on Improved YOLOX. In: **ong, N., Li, M., Li, K., **ao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_29

Download citation

Publish with us

Policies and ethics

Search

Navigation