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Automatic Detection and Quantification of Hot-Rolled Steel Surface Defects Using Deep Learning

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Abstract

Timely defect detection plays a positive role in preventing the evaluation of steel derivative damage. As a state-of-the-art image recognition technology, pixel-level segmentation technology can obtain the pixel distribution and generate the shape of the objects accurately, which provides a potential detection method for steel surface defects. Therefore, this paper employed the well-known pixel-level segmentation CNN (DeepLab_v3+) to detect three defect categories (inclusions, patches and scratches) on the steel surface, and the ‘regionprops’ function was used to quantify the defect features (length, average width, maximum width, area and ratio). The results show that ResNet50, as the backbone network of DeepLab_v3+, has the highest detection precision for steel surface defects, and its accuracy is more competitive than that of other algorithms (FCN, SegNet, U-Net and PAG-Net). The proposed quantitative method also achieved encouraging results (the average relative error (ARE) of the evaluation indicator, 10%, 18%, 17%, 23% and 23%, respectively), and the precision was higher than that of the other methods. This demonstrated that the proposed method can greatly benefit steel surface defect detection and evaluation of defect levels.

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Acknowledgements

This research was supported by the project (No. 52208309) of the National Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Civil Engineering, Guangzhou University, Guangzhou, 510006, China

    Zongchao Liu, Zeyuan Zeng & Junhui Li

  2. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Shuai Teng

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  1. Zongchao Liu
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  2. Zeyuan Zeng
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  3. Junhui Li
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Contributions

Conceptualization, ST; Data curation, XL and ZL; Formal analysis, JL; Funding acquisition, JL; Investigation, ST and XL; Methodology, ST and ZL; Project administration, ST; Software, ST; Writing – original draft, ST; Writing – review & editing, ZL.

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Correspondence to Shuai Teng.

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Liu, Z., Zeng, Z., Li, J. et al. Automatic Detection and Quantification of Hot-Rolled Steel Surface Defects Using Deep Learning. Arab J Sci Eng 48, 10213–10225 (2023). https://doi.org/10.1007/s13369-022-07567-x

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  • DOI: https://doi.org/10.1007/s13369-022-07567-x

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