Skip to main content
SpringerLink
Log in

Surface Object Detection Algorithm of Lightweight Steel Rolling Based on Machine Vision

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Aiming at the problem of a large number of parameters and large computation in deep learning for steel rolling defect detection, a lightweight object detection algorithm was proposed based on YOLOv5s improved algorithm. Firstly, the YOLOv5s trunk feature extraction network was reconstructed with a simplified Efficientnet network. Then, coordinate attention (CA) was introduced to embed in the network to highlight the useful channel information. The improved Stem structure was introduced to replace the Focus structure to enhance the shallow feature extraction ability of the network. Finally, the final prediction box was generated with the decoder of YOLOv5. The experimental results showed that compared with YOLOv5s, the number of parameters and calculation amount of the improved YOLOv5s model were reduced by 53.0% and 57.0%, respectively, the mAP (mean average precision) was up to 80.2%, and the inference time was up to 30 ms frame−1. The experimental results showed that the improved model can be used to detect steel rolling defects in production, and it was possible to deploy the algorithm to handheld object detection devices with limited computing capacity and memory.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  1. Chen J, Liu M, Fu Q, Yao Z. Surface defect detection method of hot rolled steel strip based on deep learning. Autom Inf Eng. 2019;40(04):11–6+19.

    Google Scholar 

  2. Yajun G, Yunwu Z, Guofu C. Research on the cause of welding defects of tc128 marine steel based on nondestructive testing technology. Chem Eng Equip. 2016;11:207–9.

    Google Scholar 

  3. Yang J, Duan M. Roll surface damage detection based on image recognition system. J Forg Technol Ecko. 2021;6:225–30. https://doi.org/10.13330/j.issn.1000-3940.2021.06.031.

    Article  Google Scholar 

  4. Li W, Huang X, Wang M, Wan G. Strip surface defect detection system based on machine vision. J Huazhong Univ Sci Technol Nat Sci Ed. 2003;2:72–4. https://doi.org/10.13245/j.hust.2003.02.025.

    Article  Google Scholar 

  5. Moganti M, Ercal F, Dagli CH, et al. Automatic PCB inspection algorithms: a survey. Comput Vis Image Underst. 1996;63(2):287–313.

    Article  Google Scholar 

  6. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR 2014: proceedings of the 2014 IEEE conference on computer vision and pattern recognition. Piscataway: IEEE; 2014. pp. 580–587.

  7. Girshick R. Fast R-CNN. In: ICCV 2015: proceedings of the 2015 IEEE international conference on computer vision. Piscataway: IEEE; 2015. pp. 1440–1448.

  8. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-t ime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(6):1137–49.

    Article  Google Scholar 

  9. Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection. In: CVPR 2016: proceedings of the 2016 IEEE conference on computer vision and pattern recognition. Piscataway: IEEE; 2016. pp. 779–788.

  10. Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: CVPR 2017: proceedings of the 2017 IEEE conference on computer vision and pattern recognition. Piscataway: IEEE; 2017. pp. 7263–7271.

  11. Redmon J, Farhadi A. Yolov3: an incremental improvement. 2021. https://arxiv.org/pdf/1804.02767.pdf.

  12. Bochkovskiy A, Wang C Y, Liao H. Yolov4: optimal speed and accuracy of object detection. 2020. https://arxiv.org/abs/2004.10934v1.

  13. Zhu X, Tian S, Fan G, Wang Z. Research on aircraft carrier surface multi-scale object detection based on improved SSD algorithm. Ship Electron Eng. 2022;42(02):42–7.

    Google Scholar 

  14. Sandler M, Howard A, Zhu M, et al. Mobilenetv2: inverted residuals and linear bottlenecks. In CVPR 2018: proceedings of the 2018 IEEE conference on computer vision and pattern recognition. Piscataway: IEEE; 2018. pp. 4510–4520.

  15. Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3. In: ICCV 2019: proceedings of the IEEE international conference on computer Vision. Piscataway: IEEE; 2019. pp 1314–1324.

  16. Qi R, Jia R, Xu Z, et al. A lightweight object detection network based on YOLOv3. Comput Appl Softw. 2020;37(10):208–13.

    Google Scholar 

  17. Jin Y, Wen Y, Liang J (2020) Embedded real-time pedestrian detection system using YOLO optimized by LNN. In: ICECCE 202 0:2020 international conference on electrical, communication, and computer engineering. Piscataway: IEEE; 2020. pp. 1–5.

  18. Huang J, Zuo H, Zhang J. Research and application of lightweight object detection algorithm. Comput Eng. 2021. https://doi.org/10.19678/j.issn.1000-3428.0059168.

    Article  Google Scholar 

  19. Shao W, Wang X, Cao Z, et al. Design of lightweight convolutional neural network based on MobileNet and YOLOv3. J Comput Appl. 2020;40(S1):8–13.

    Google Scholar 

  20. Li Y, Wang X. Surface defect detection of steel rolling based on YOLOv5s model. Manuf Autom. 2019;43(11):117–9.

    Article  Google Scholar 

  21. Liu K, He J, Zhang Y, et al. Improving YOLO’s vehicle detection algorithm. Mod Electron Tech. 2019;42(13):47–50.

    Google Scholar 

  22. Wang CY, Liao HYM, Wu YH, et al. CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020. pp, 390–391.

  23. Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR; 2019. pp. 6105–6114.

  24. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. pp. 13713–13722.

  25. Wang RJ, Li X, Ling CX. Pelee: a real-time object detection system on mobile devices. Adv Neural Inf Process Syst. 2018. https://doi.org/10.48550/arXiv.1804.06882.

    Article  Google Scholar 

  26. Northeastern University. Steel surface data set: NU-CLS [DB/OL]. http://faculty.neu.edu.cn/yunhyan/NEU_surface_defect_database.html.

Download references

Funding

This work is supported in part by the Natural Science Foundation of China under Grant (61771188).

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, Hubei, China

    Zhifeng Zhong, Gao Wang, Jianjvn Tan & Yifan Xia

Authors
  1. Zhifeng Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianjvn Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yifan Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gao Wang.

Ethics declarations

Conflict of interest

The authors state that no conflict of interest exists.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Zhong, Z., Wang, G., Tan, J. et al. Surface Object Detection Algorithm of Lightweight Steel Rolling Based on Machine Vision. SN COMPUT. SCI. 4, 806 (2023). https://doi.org/10.1007/s42979-023-02271-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02271-5

Keywords

Search

Navigation