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Metal sensor base defects detection using deep learning based YOLO network

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Abstract

Currently, the existing object detection methods have many limitations in detecting defects on the base surface of metal sensors, such as a high rate of false detection and missed detection. Therefore, we proposed an improved algorithm based on You Look Only Once (YOLO) v5s aiming to solve the problem. Firstly, the C3 module was poor at detecting small defects. To enrich the gradient flow information and improve the detection accuracy of defects, the C2f module was used to replace part of the C3 module in the neck of the YOLO v5s. Then, an improved attention mechanism named Dilated Global Attention Mechanism was proposed to make the network focus more on the important information features. The dilated convolution was integrated into the spatial attention mechanism to enhance the receptive field of the model, reduce the model size and improve the detection performance of small defects. Finally, we proposed a novel localization loss function named Intersection over Union (IoU) with Normalized Wasserstein Distance, which not only alleviated the issue of Complete IoU loss based metrics being sensitive to the location deviations of small defects but also adjusted to diverse datasets. Results from ablation experiments demonstrated that the improved YOLO v5s algorithm enhanced the detection of the mean Average Precision by 5.3% and the Precision rate (P) by 7% compared with the original algorithm.

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No datasets were generated or analysed during the current study.

References

  1. Mordia, R.: Visual techniques for defects detection in steel products: a comparative study. Eng. Fail. Anal. 134, 106047 (2022)

    Article  MATH  Google Scholar 

  2. Cheng, J.Y.: Research on metal surface defect detection by improved YOLOv3. Comput. Eng. Appl. 57(19), 252–258 (2021)

    MATH  Google Scholar 

  3. Ren, S.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)

    Article  MATH  Google Scholar 

  4. Girshick, R.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

  5. He, K.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  MATH  Google Scholar 

  6. Liu, W.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference. Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016)

  7. Redmon, J.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  8. Redmon, J.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

  9. Redmon, J.: Yolov3: An Incremental Improvement. arXiv:1804.02767 (2018)

  10. Bochkovskiy, A.: Yolov4: Optimal Speed and Accuracy of Object Detection. arXiv:2004.10934 (2020)

  11. Ge, Z.: Yolox: Exceeding Yolo Series in 2021. arXiv:2107.08430 (2021)

  12. Wang, C. Y.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

  13. Zhang, B.: Research on surface defect detection of rare-earth magnetic materials based on improved SSD. Complexity 1, 4795396 (2021)

    Article  Google Scholar 

  14. Liu, S.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)

  15. Yang, M.: A deep learning model S-Darknet suitable for small target detection. J. Phys. Conf. Ser. 1871(1), 012118 (2021)

    Article  Google Scholar 

  16. Wang, M.: FE-YOLOv5: feature enhancement network based on YOLOv5 for small object detection. J. Vis. Commun. Image R 90, 103752 (2023)

    Article  MATH  Google Scholar 

  17. Li, C.: Steel surface defect detection method based on improved YOLOX. IEEE Access (2024)

  18. Yang, R.: KPE-YOLOv5: an improved small target detection algorithm based on YOLOv5. Electronics 12(4), 817 (2023)

    Article  MATH  Google Scholar 

  19. Wang, C. Y.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)

  20. Lin, T. Y.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  21. Hu, J.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  22. Wang, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)

  23. Woo, S.: CBAM: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

  24. Liu, Y.: Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions. arXiv:2112.05561 (2021)

  25. Zheng, Z.: Distance-IoU loss: Faster and better learning for bounding box regression. Proc. AAAI Conf. Artif. Intell. 34(7), 12993–13000 (2020)

    Google Scholar 

  26. Wang, J.: A normalized Gaussian Wasserstein Distance for Tiny Object Detection. arXiv:2110.13389 (2021)

  27. Zhu, X.: Enhanced feature Fusion structure of YOLO v5 for detecting small defects on metal surfaces. Int. J. Mach. Learn. Cybern. 14(6), 2041–2051 (2023)

    Article  MATH  Google Scholar 

  28. Xiao, D.: A detection method of spangle defects on zinc-coated steel surfaces based on improved YOLO-v5. Int. J. Adv. Manuf. Technol. 128(1–2), 937–951 (2023)

    Article  MATH  Google Scholar 

  29. Zhou, C.: Metal surface defect detection based on improved YOLOv5. Sci. Rep. 13(1), 20803 (2023)

    Article  Google Scholar 

  30. Lv, Z.: LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces. Eng. Appl. Artif. 133, 108482 (2024)

    Article  MATH  Google Scholar 

  31. Tan, M.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

  32. Wang, C. Y.: Designing Network Design Strategies Through Gradient Path Analysis. arXiv:2211.04800 (2022)

  33. Liu, K.: Underwater target detection based on improved YOLOv7. J. Mar. Sci. Eng. 11(3), 677 (2023)

    Article  MATH  Google Scholar 

  34. Wang, X.: Single shot multibox detector object detection based on attention mechanism and feature fusion. J. Electron. Imaging 32(2), 023032–023032 (2023)

    Article  Google Scholar 

  35. Lin, T. Y. Microsoft coco: common objects in context. In: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13, pp. 740–755 (2014)

  36. Deng, J.: Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. 248–255 (2009)

  37. Cao, C.: A Survey of Mix-Based Data Augmentation: Taxonomy, Methods, Applications, and Explainability. arXiv:2212.10888 (2022)

  38. Ding, K.: Data augmentation for deep graph learning: a survey. ACM SIGKDD Exp. Newsl. 24(2), 61–77 (2022)

    Article  MATH  Google Scholar 

  39. Kumar, T.: Advanced Data Augmentation Approaches: A Comprehensive Survey and Future Directions. arXiv:2301.02830 (2023)

  40. Hou, Q.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713–13722 (2021)

  41. Rezatofighi, H.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)

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Acknowledgements

The paper work was supported by Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology (FMZ201901), and the National Natural Science Foundation of China “Research on bionic chewing robot for physical property detection and evaluation of food materials” (51375209).

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Authors and Affiliations

  1. Jiangnan University, Wuxi, 214122, Jiangsu, China

    Bufan Zhang, Xingfei Zhu, Jinghu Yu, Zhaofei Sun & Qimeng Wang

  2. The Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi, 214122, Jiangsu, China

    Bufan Zhang, Xingfei Zhu, Jinghu Yu, Zhaofei Sun & Qimeng Wang

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  1. Bufan Zhang
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Contributions

Bufan Zhang, Xingfei Zhu and Jinghu Yu performed conceptualization and methodology. Bufan Zhang, Xingfei Zhu, Zhaofei Sun and Qimeng Wang performed data curation. Bufan Zhang, Xingfei Zhu, Jinghu Yu, Zhaofei Sun and Qimeng Wang performed formal analysis. Bufan Zhang and Xingfei Zhu performed draft manuscript. Bufan Zhang, Xingfei Zhu and Jinghu Yu performed supervision. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Jinghu Yu.

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Zhang, B., Zhu, X., Yu, J. et al. Metal sensor base defects detection using deep learning based YOLO network. SIViP 19, 47 (2025). https://doi.org/10.1007/s11760-024-03685-1

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