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A steel surface defect inspection approach towards smart industrial monitoring

Author

Listed:
  • Ruiyang Hao

    (Tsinghua University)

  • Bingyu Lu

    (Tsinghua University)

  • Ying Cheng

    (Beihang University)

  • Xiu Li

    (Tsinghua University)

  • Biqing Huang

    (Tsinghua University)

Abstract

With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely used in manufacturing equipment, and steel surface defect inspection is of great significance to the normal operation of steel equipment in manufacturing workshops. In steel defect inspection systems, industrial inspection robots generate images via scanning steel surface, and processors perform surface defect inspection algorithms on images. We focus on applying advanced object detection techniques to surface defect inspection algorithm for sheet steel. In the proposed steel surface defect inspection model, a deformable convolution enhanced backbone network firstly extracts complex features from multi-shape steel surface defects. Then the feature fusion network with balanced feature pyramid generates high-quality multi-resolution feature maps for the inspection of multi-size defects. Finally, detector network achieves the localization and classification of steel surface defects. The proposed model is evaluated on a typical steel surface defect dataset. Our model achieves 0.805 mAP, 0.144 higher than baseline models, and our model shows high efficiency in inference. Experiments are performed to reveal the effect of employed approaches, and results also show our model achieves a balance between inspection performance and inference efficiency.

Suggested Citation

  • Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01670-2
    DOI: 10.1007/s10845-020-01670-2
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    References listed on IDEAS

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    1. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
    2. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    3. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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    Citations

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    Cited by:

    1. José M. Navarro-Jiménez & José V. Aguado & Grégoire Bazin & Vicente Albero & Domenico Borzacchiello, 2023. "Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2345-2358, June.
    2. Shuai Ma & Kechen Song & Menghui Niu & Hongkun Tian & Yunhui Yan, 2024. "Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 367-386, January.
    3. Li Wei & Mahmud Iwan Solihin & Sarah ‘Atifah Saruchi & Winda Astuti & Lim Wei Hong & Ang Chun Kit, 2024. "Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review," SN Operations Research Forum, Springer, vol. 5(3), pages 1-71, September.
    4. Zhuxi Ma & Yibo Li & Minghui Huang & Qianbin Huang & Jie Cheng & Si Tang, 2023. "Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2431-2447, June.

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