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DF-YOLOv7: steel surface defect detection based on focal module and deformable convolution

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Abstract

Steel is a fundamental material in the manufacturing process, and the quality of the steel used directly affects the quality of the final product. During the manufacturing process, a variety of complex and irregular defects may form on the surface of the steel. In order to detect these defects, this paper proposes the DF-YOLOv7 model. The model employs the K-means +  + algorithm to adjust the anchor box sizes across datasets, thereby enhancing the extraction of features for different defects. Furthermore, the D-SPPCSPC module is employed to enhance defect detection and reduce model parameters. Additionally, the CIoU Loss with Focal module addresses positive–negative sample imbalance by focusing on high-quality anchor boxes. Experimental results demonstrate that the proposed model achieves an mAP of 0.771 on the NEU-DET dataset, representing a 3.6% improvement over the original model. It outperforms some state-of-the-art detectors and meets the real-time industrial detection requirements.

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Data availability

We conducted experiments on NEU-DET dataset. The dataset can be found in http://faculty.neu.edu.cn/songkechen/zh_CN/zhym/263-269/list/index.htm.

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Funding

This work was supported by the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0605), and Science and Technology Foundation of Chongqing Education Commission (Grant No. KJQN202001137).

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Authors and Affiliations

Authors

Contributions

Conceptualization, T.H. and W.Z.; methodology, W.Z.; software, Q.Y. and Y.H.; validation, W.Z. and S.L.; formal analysis, T.H.; investigation, W.Z. and J.X.; resources, T.H.; data curation, S.L. and Y.H.; writing—original draft preparation, W.Z. and J.X.; writing—review and editing, T.H. and Y.X.; visualization, W.Z. and J.X.; supervision, T.H. and Y.X.; project administration, T.H.; funding acquisition, T.H. All authors reviewed the manuscript.

Corresponding author

Correspondence to Tongyuan Huang.

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The authors declare no competing interests.

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NEU-DET dataset belong to public datasets. User can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues.

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Zhang, W., Huang, T., Xu, J. et al. DF-YOLOv7: steel surface defect detection based on focal module and deformable convolution. SIViP 19, 97 (2025). https://doi.org/10.1007/s11760-024-03679-z

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  • DOI: https://doi.org/10.1007/s11760-024-03679-z

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