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A detection method of spangle defects on zinc-coated steel surfaces based on improved YOLO-v5

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Abstract

The hot-dip galvanizing process is widely used in the production of steel products as an important anti-corrosion method for steel products. Due to the complexity of the galvanizing process and the immaturity of some technologies, some factory productions of hot-dip galvanizing products are often with defects such as unsuitable spangle size in the head and tail, unsuitable spangle size in the edge, diagonal edge, and penetration streaks (zinc flow lines), which seriously affect the appearance of the zinc layer and subsequent using function, and are likely to cause undesirable consequences. In order to solve these problems, the object detection method in the field of deep learning is applied to the zinc spangles defects detection. Firstly, a dataset of zinc spangles defects on zinc-coated steel surfaces was established, and the dataset is preprocessed in this paper. Data enhancement is used to improve the generalization ability of the model, and Wiener filter is used to reduce the motion blur. This paper combines transformer structure and Bi-FPN structure with the YOLO-v5 model to propose the YOLOv5-Transformer-BiFPN (YOLOv5-TB) model. The YOLOv5-TB model is used for zinc-coated steel defect detection, which can accurately and quickly detect spangles defects on the surface of zinc-coated steel. The experimental results show that the overall performance of YOLOv5-TB network applied to the detection of zinc spangles defects on zinc-coated steel surfaces is better than most of the current mainstream Object detection algorithms.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 52074064 and Grant 62173073; in part by the Fundamental Research Funds for Liaoning Natural Science Foundation, China, under Grant 2019-MS-120; in part by the Fundamental Research Funds for the Central Universities, China under Grant N2204006 Grant N2104026, Grant N2018008, and Grant N2001002; and in part by the National Key Research and Development Program of China under 2020AAA0109203; in part by the Natural Science Foundation of Science and Technology Department of Liaoning Province (2021-BS-054).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Dong Xiao, Fei Tong Xie, Zhen Ni Li & Hong Fei Xie

  2. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, 110819, China

    Yang Gao

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  1. Dong Xiao
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  2. Fei Tong Xie
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  4. Zhen Ni Li
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  5. Hong Fei Xie
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Contributions

All authors contributed to the study conception and design. Conceptualization, writing—original draft, and methodology were done by Fei Tong Xie. Writing—review and editing, data curation, and formal analysis were performed by Dong Xiao. Resources and funding acquisition were performed by Yang Gao and Zhen Ni Li. Review, editing, and validation were performed by Hong Fei Xie. All authors read and approved the final manuscript.

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Correspondence to Dong Xiao.

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Xiao, D., Xie, F.T., Gao, Y. et al. A detection method of spangle defects on zinc-coated steel surfaces based on improved YOLO-v5. Int J Adv Manuf Technol 128, 937–951 (2023). https://doi.org/10.1007/s00170-023-11963-4

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