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An industrial defect detection algorithm based on CPU-GPU parallel call

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Abstract

The workpiece positioning and defect segmentation are two key steps in the workpiece detection process. This paper has designed a CPU-GPU parallel call algorithm based on real industrial quality inspection conditions to realize the high-speed workpiece defect detection. The algorithm can fully utilize all computing resources of the industrial control computer while simultaneously accomplishing the workpiece positioning and defect segmentation tasks. Moreover, to reduce the workpiece defect detection's scope and improve the defect segmentation algorithm's efficiency, the proposed method uses the workpiece positioning results. As for the positioning task, we have designed the double pyramid method to enhance the positioning speed. When it comes to the defect segmentation task, we have introduced the lightweight network to improve the workpiece segmentation speed. Considering that the current general data sets are of the workpiece local image(s) post cutting, we set up a new data set to reflect the situation in the industrial field. It consists of images taken from real industrial fields that can better verify the whole quality inspection algorithm process, including the positioning and segmentation algorithms. According to our experiment, our algorithm accomplished the positioning and defect segmentation tasks at a speed of 116FPS. Additionally, the segmentation accuracy reached 75.12% Mean IoU.

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References

  1. Bao Y, Song K, Liu J et al (2021) Triplet-graph reasoning network for few-shot metal generic surface defect segmentation[J]. IEEE Trans Instrum Measur 70:1–11

    Google Scholar 

  2. Bulnes FG, Usamentiaga R, Garcia DF, Molleda J (2016) An efficient method for defect detection during the manufacturing of web materials. J Intell Manuf 27(2):431–445

    Article  Google Scholar 

  3. Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//computer vision–ECCV 2018:15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII 15. Springer International Publishing, pp  833–851

  4. Chen F, Ye X, Yin S et al (2019) Automated vision positioning system for dicing semiconductor chips using improved template matching method[J]. Int J Adv Manuf Technol 100(9):2669–2678

    Article  Google Scholar 

  5. He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]//2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society 1026–1034

  6. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society 770–778.

  7. Huang Y, Qiu C, Yuan K (2020) Surface defect saliency of magnetic tile. Vis Comput 36(1):85–96

    Article  MathSciNet  Google Scholar 

  8. Huang Y, Jing J, Wang Z (2021) Fabric defect segmentation method based on deep learning[J]. IEEE Trans Instrum Measur 70:1–15

    Google Scholar 

  9. Jing J, Wang Z, Rätsch M et al (2022) Mobile-Unet: An efficient convolutional neural network for fabric defect detection[J]. Textile Res J 92(1–2):30–42

    Article  Google Scholar 

  10. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1097–1105

    Google Scholar 

  11. Li X, Su H, Liu G (2020) Insulator defect recognition based on global detection and local segmentation. IEEE Access 8:59934–59946. https://doi.org/10.1109/ACCESS.2020.2982288

    Article  Google Scholar 

  12. Lin H, Li B, Wang X, Shu Y, Niu S (2019) Automated defect inspection of LED chip using deep convolutional neural network. J Intell Manuf 30(6):2525–2534

    Article  Google Scholar 

  13. Liu J, Bu F (2019) Improved RANSAC features image-matching method based on SURF. J Eng 2019(23):9118–9122

    Article  Google Scholar 

  14. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society 3431–3440

  15. Luo G, Zhou RG, Liu X, Hu W, Luo J (2018) Fuzzy matching based on gray-scale difference for quantum images. Int J Theor Physics 57(8):2447–2460

    Article  MathSciNet  MATH  Google Scholar 

  16. Monari J, Montebugnoli S, Orlati A et al (2006) Generalized Hough transform: A useful algorithm for signal path detection[J]. Acta Astronautica 58(4):230–235

    Article  Google Scholar 

  17. Oztemel E, Gursev S (2020) Literature review of Industry 4.0 and related technologies. J Intell Manuf 31(1):127–182

    Article  Google Scholar 

  18. Paniagua B, Vega-Rodríguez MA, Gomez-Pulido JA, Sanchez-Perez JM (2010) Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing. J Intell Manuf 21(6):745–760

    Article  Google Scholar 

  19. Racki D, Tomazevic D, Skocaj D (2018) A compact convolutional neural network for textured surface anomaly detection[C]//2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE Computer Society 1331–1339

  20. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst 28:91–99. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  21. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241

    Google Scholar 

  22. Sedaghat A, Mohammadi N (2019) High-resolution image registration based on improved SURF detector and localized GTM. Int J Remote Sens 40(7):2576–2601. https://doi.org/10.1080/01431161.2018.1528402

    Article  Google Scholar 

  23. Silvestre-Blanes J, AlberoAlbero T, Miralles I, Pérez-Llorens R, Moreno J (2019) A public fabric database for defect detection methods and results. Autex Res J 19(4):363–374. https://doi.org/10.2478/aut-2019-0035

    Article  Google Scholar 

  24. Tabernik D, Šela S, Skvarč J, Skočaj D (2020) Segmentation-based deep-learning approach for surface-defect detection. J Intell Manuf 31(3):759–776

    Article  Google Scholar 

  25. Tao X, Zhang D, Ma W, Liu X, Xu D (2018) Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl Sci 8(9):1575. https://doi.org/10.3390/app8091575

    Article  Google Scholar 

  26. Weimer D, Scholz-Reiter B, Shpitalni M (2016) Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals 65(1):417–420. https://doi.org/10.1016/j.cirp.2016.04.072

    Article  Google Scholar 

  27. Wolfschläger D, Woltersmann JH, Montavon B et al (2022) Sheared edge defect segmentation using a convolutional U-Net for quantified quality assessment of fine blanked workpieces[J]. Precis Eng 75:129–141

    Article  Google Scholar 

  28. Yang YG, Zhao QQ, Sun SJ (2015) Novel quantum gray-scale image matching. Optik 126(22):3340–3343. https://doi.org/10.1016/j.ijleo.2015.08.010

    Article  Google Scholar 

  29. Yongfei Z, Tong Z (2021) A method of workpiece location based on improved generalized Hough transform. J Phys: Conf Ser 1939(1):012079

  30. Yu C, Wang J, Peng C et al (2018) BiSeNet: Bilateral segmentation network for real-time semantic segmentation[C]//computer vision–ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII. Springer International Publishing, Cham, pp 334–349

  31. Yu C, Gao C, Wang J, Yu G, Shen C, Sang N (2021) Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation. Int J Comput Vis 129(11):3051–3068

    Article  Google Scholar 

  32. Zhong F, He S, Li B (2017) Blob analyzation-based template matching algorithm for LED chip localization. Int J Adv Manuf Technol 93(1):55–63

    Article  Google Scholar 

Download references

Funding

This work was supported by the Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, China.

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Authors and Affiliations

  1. School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310000, China

    Zhu Li, Hong-wei Lin, Yuan-yuan Liu, Chong Chen & Yun-fei Xia

Authors
  1. Zhu Li
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  2. Hong-wei Lin
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  3. Yuan-yuan Liu
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  4. Chong Chen
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  5. Yun-fei Xia
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Contributions

Conceptualization: Z.L. and H.L.

Formal analysis: Z.L. and H.L.

Investigation: H.L., Z.L. and Y.L..

Methodology: Z.L. and C.C.

Project administration: Z.L.

Resources: H.L.

Validation: H.L.

Writing—original draft: H.L. and Z.L.

Writing—review & editing: C.C.,Y.X. and H.L.

Corresponding author

Correspondence to Hong-wei Lin.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Li, Z., Lin, Hw., Liu, Yy. et al. An industrial defect detection algorithm based on CPU-GPU parallel call. Multimed Tools Appl 82, 44191–44207 (2023). https://doi.org/10.1007/s11042-023-15613-5

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  • DOI: https://doi.org/10.1007/s11042-023-15613-5

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