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GSC-YOLO: a lightweight network for cup and piston head detection

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Abstract

In order to improve the detection effect of the cup and piston head during the operation of the automatic cupping machine and reduce the blurring effect of the robotic arm movement on the collected images, we propose a network called GSC-YOLO. Firstly, we introduce GhostNet as the backbone network and SimSPPF as the spatial pyramid pooling method to improve the network lightweight, and then we add the CA attention mechanism to strengthen the feature extraction ability of the network and expand the receptive field. Secondly, we add a small target detection layer to enhance the detection ability of the piston head. Finally, the data enhancement is carried out by affine transformation and motion blur processing of the dataset, which improves the robustness of the model for long-distance and vibration environment detection. The experimental results show that in the comparison model, GSC-YOLO has the smallest size, the best detection effect, and faster detection speed, where, the detection accuracy is 0.967, the recall is 0.945, and the inference speed of each image on the CPU and GPU is 175.1 ms and 12.4 ms, so this model can be used for real-time detection of cup bodies and piston heads.

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Data availability

These data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Chirali, I.Z.: Cosmetic cupping therapy. In: Chirali, I.Z. (ed.) Traditional Chinese medicine cupping therapy, 3rd edn., pp. 123–143. Elsevier, Amsterdam (2014)

    Chapter  Google Scholar 

  2. Liu, Y.B., Qin, J.H., Zhu, M.Y., et al.: Fast stitching method for multi-view images of cupping scups. Sig. Image Video Process. (2023). https://doi.org/10.1007/s11760-022-02402-0

    Article  Google Scholar 

  3. Zhang, R., Shao, Z., Huang, X., et al.: Adaptive dense pyramid network for object detection in UAV imagery. Neurocomputing 489, 377–389 (2022)

    Article  Google Scholar 

  4. Zhai, H., Zhang, Y.: Target detection of low-altitude uav based on improved yolov3 network. J. Robot. (2022). https://doi.org/10.1155/2022/4065734

    Article  Google Scholar 

  5. Chen, W., Zhao, Y., You, T., et al.: Automatic detection of scattered garbage regions using small unmanned aerial vehicle low-altitude remote sensing images for high-altitude natural reserve environmental protection. Environ. Sci Technol. 55(6), 3604–3611 (2021)

    Article  Google Scholar 

  6. Wang, B., Wang, R., Tang, B., et al.: A research on advanced technology of target detection in unmanned driving. J. Phys. Conf. Ser. 2010(1), 012158 (2021)

    Article  Google Scholar 

  7. Yan, C., et al.: Age-invariant face recognition by multi-feature fusionand decomposition with self-attention. ACM Trans. Multimed. Comput. Commun. Appl. 18, 1–18 (2022)

    Article  Google Scholar 

  8. Wasala, M., Kryjak, T.: Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream. (2022)

  9. Goyal, S.: Genetic evolution-based feature selection for software defect prediction using SVMs. J. Circuits Syst. Comput. 31(11), 2250161 (2022)

    Article  Google Scholar 

  10. Wang, K., Ding, C., Maybank, S.J., et al.: CDPM: convolutional deformable part models for semantically aligned person re-identification. IEEE Trans. Image Process. 29, 3416–28 (2019)

    Article  Google Scholar 

  11. Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentatio. In: IEEE computer society, (2013)

  12. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  13. Berg, A.C., Fu, C.Y., Szegedy, C., et al.: SSD: single shot multibox detector. Springer, Berlin (2015). https://doi.org/10.1007/978-3-319-46448-0_2

    Book  Google Scholar 

  14. Redmon, J., Divvala, S., Girshick, R., et al. You only look once: unified, real-time object detection. In: Computer vision & pattern recognition (2016)

  15. Hu, X., Liu, Y., Zhao, Z., et al.: Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Comput. Electron. Agric. 185(8), 106135 (2021)

    Article  Google Scholar 

  16. Wang, Z., Jin, L., Wang, S., et al.: Apple stem/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loading system. Postharvest Biol. Technol. 185, 111808 (2022)

    Article  Google Scholar 

  17. Zhang, L., Li, C., Sun, H.: Object detection/tracking toward underwater photographs by remotely operated vehicles (ROVs). Future Gener. Comput. Syst. 126, 163–168 (2022)

    Article  Google Scholar 

  18. Wang, X., Zhao, Q., Jiang, P., Zheng, Y., Yuan, L., Yuan, P.: LDS-YOLO: a lightweight small object detection method for dead trees from shelter forest. Comput. Electron. Agric. 198, 107035 (2022)

    Article  Google Scholar 

  19. Han, K., Wang, Y., Tian, Q., et al. GhostNet: more features from cheap operations. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, (2020)

  20. Chen, C., Liu, M.Y., Tuzel, O., et al.: R‑CNN for small object detection. In: Proceeding of Asian conference on computer vision. pp. 214‑230. Springer, Cham (2016)

  21. Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft COCO: common objects in context. in: proceedings of european conference on computer vision. pp. 740‑755 Springer, Cham (2014)

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

  23. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141. (2018)

  24. Woo, S., Park, J., Lee J.Y. et al.: CBAM: convolution block attention module. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3–19 (2018)

  25. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  26. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7 : trainable bag-of freebies sets new state-of-the-art for realtime object detectors[EB/OL]. https: //arxiv.org/abs/2207. 02696.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 61961011) and the National Natural Science Foundation of China (No. 61650106).

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Authors and Affiliations

  1. School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541004, China

    Ying-Bin Liu & Jian-Hua Qin

  2. School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004, China

    Yu-Hui Zeng

Authors
  1. Ying-Bin Liu
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  2. Yu-Hui Zeng
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  3. Jian-Hua Qin
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Contributions

Y-BL contributed to methodology, investigation, formal analysis, writing—original draft, and supervision. Y-HZ worked in investigation, formal analysis, writing—review and editing, project administration. J-HQ helped in conceptualization, methodology, and visualization.

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Correspondence to Jian-Hua Qin.

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All authors declare that they have no conflict of interest.

Ethical approval

The study protocol was approved by the ethics review board of Guilin University of Technology. All of the procedures were performed in accordance with the Declaration of Helsinki and relevant policies in China.

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Liu, YB., Zeng, YH. & Qin, JH. GSC-YOLO: a lightweight network for cup and piston head detection. SIViP 18, 351–360 (2024). https://doi.org/10.1007/s11760-023-02746-1

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