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A marigold corolla detection model based on the improved YOLOv7 lightweight

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Abstract

Marigold, as an important traditional Chinese medicinal plant renowned for its therapeutic attributes in heat dissipation, detoxification, liver protection, facial enhancement, and promotion of eye health, has witnessed a surge in demand. The demand for marigold is steadily rising, necessitating the inevitable shift toward mechanized harvesting in the industrialization of marigold cultivation. Consequently, this study endeavors to assemble a novel dataset in southern Xinjiang, China, crucial for marigold production. The focus is on improving the YOLOv7 model by lightweighting and proposing a set of detection models applicable to marigold. Through the elimination of superfluous object detection layers in the YOLOv7 model, substitution of conventional backbone network convolution with DSConv, replacement of the SPPCSPC module with Simplified SPPF, and subsequent model pruning and then retraining. This study aims to address challenges related to the deployment of mobile devices on the marigold-picking robot and the realization of high real-time detection. The experimental results demonstrate that the enhanced YOLOv7 model exhibited exceptional precision and mAP0.5 in marigold detection, achieving 93.9% and 97.7%, respectively, surpassing the performance of the original YOLOv7 model. Notably, the GFLOPs are a mere 2.3, representing only 2.2% of the computational load of the original model. Moreover, the parameter has been reduced to 15.04M, representing only 41.2% of the original model. The attained FPS, reaching 166.7, signifies a noteworthy enhancement of 26.7% compared to the original model. This showcases exceptional precision and speed in marigold detection, providing a strong technical foundation for the efficient harvesting of marigolds.

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References

  1. Li, N., Wang, P., Wu, Z., et al.: Research status and development prospects on marigold. Northern Hortic. 10, 228–231 (2010). ((in Chinese))

  2. Zhengdong, Q., Guodang, L., Liangliang, L., et al.: Present situation and suggestions on mechanical picking technology of marigold. J. Real-Time Image Proc. 10(09), 26–30 (2020)

    Google Scholar 

  3. 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). https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  4. Redmon, J., Divvala, S., Girshick, R., et al. (2016) You only look once: Unified, real-time object detection. In: IEEE Computer Society, Los Alamitos, CA, USA, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91

  5. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) https://doi.org/10.1109/CVPR.2017.690

  6. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv e-prints (2018)

  7. Li, C., Ma, W., Liu, F., et al.: Recognition of citrus fruit and planning the robotic picking sequence in orchards. SIViP 17(8), 4425–4434 (2023)

    Article  Google Scholar 

  8. Gai, R., Chen, N., Yuan, H.: A detection algorithm for cherry fruits based on the improved yolo-v4 model. Neural Comput. Appl. 35(19), 13895–13906 (2021)

    Article  Google Scholar 

  9. Yu, K., Tang, G., Chen, W., et al.: Mobilenet-yolo v5s: an improved lightweight method for real-time detection of sugarcane stem nodes in complex natural environments. IEEE Access 11, 104070–104083 (2023). https://doi.org/10.1109/ACCESS.2023.3317951

    Article  Google Scholar 

  10. Liu, L., Li, P.: An improved yolov5-based algorithm for small wheat spikes detection. SIViP 17(8), 4485–4493 (2023)

    Article  Google Scholar 

  11. Chen, J., Wang, Z., Wu, J., et al.: An improved yolov3 based on dual path network for cherry tomatoes detection. J. Food Process Eng. 44(10), e13803 (2021)

    Article  Google Scholar 

  12. Susa, JAB., Nombrefia, WC., Abustan, AS., et al. Deep learning technique detection for cotton and leaf classification using the yolo algorithm. In: 2022 International Conference on Smart Information Systems and Technologies (SIST), https://doi.org/10.1109/SIST54437.2022.9945757(2022)

  13. Qi, C., Gao, J., Pearson, S., et al.: Tea chrysanthemum detection under unstructured environments using the tc-yolo model. Expert Syst. Appl. 193, 116473 (2022). https://doi.org/10.1016/j.eswa.2021.116473

    Article  Google Scholar 

  14. Liu, Q., Wang, S., He, X., et al.: Pear flower recognition based on yolo v5s target detection model in complex orchard scenes. Springer, Singapore (2023)

    Book  Google Scholar 

  15. Wu, D., Lv, S., Jiang, M., et al.: Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agric. 178, 105742 (2020). https://doi.org/10.1016/j.compag.2020.105742

  16. Omer, S.M., Ghafoor, K.Z., Askar, S.K.: Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning. Signal Image Video Process 18, 1–14 (2023)

    Google Scholar 

  17. Qi, F., Wang, Y., Tang, Z., et al.: Real-time and effective detection of agricultural pest using an improved yolov5 network. J. Real-Time Image Proc. 20(2), 33 (2023)

    Article  Google Scholar 

  18. Xu, Y., Chen, Q., Kong, S., et al.: Real-time object detection method of melon leaf diseases under complex background in greenhouse. J. Real-Time Image Proc. 19(5), 985–995 (2022)

    Article  Google Scholar 

  19. Wang, Y., Xiao, M., Wang, S., et al.: Detection of famous tea buds based on improved yolov7 network. Agriculture 13(6), 1190 (2023)

    Article  Google Scholar 

  20. Yang, H., Liu, Y., Wang, S., et al.: Improved apple fruit target recognition method based on yolov7 model. Agriculture 13(7), 1278 (2023)

    Article  Google Scholar 

  21. Wang, CY., Bochkovskiy, A., Liao, HYM.: 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 (2023)

  22. Wang, CY., Liao, HYM., Yeh, IH.: Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800 (2022)

  23. Nascimento, MGd., Fawcett, R., Prisacariu, VA.: Dsconv: Efficient convolution operator. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

  24. Li, C., Li, L., Jiang, H., et al. Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)

  25. Li, H., Kadav, A., Durdanovic, I., et al.: Pruning filters for efficient convnets. (2016). https://doi.org/10.48550/arXiv160808710

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Funding

The National Natural Science Foundation of China (Project No. 12162031), State Key Laboratory for Manufacturing Systems Engineering of Xi’an Jiaotong University (Project No. sklms2022022).

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Authors and Affiliations

  1. College of Mechanical Engineering, Xinjiang University, Urumqi, 830017, Xinjiang, China

    Yixuan Fan, Gulbahar Tohti, Mamtimin Geni, Guohui Zhang & Jiayu Yang

  2. Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Gulbahar Tohti

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  1. Yixuan Fan
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  2. Gulbahar Tohti
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  3. Mamtimin Geni
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  4. Guohui Zhang
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  5. Jiayu Yang
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Correspondence to Gulbahar Tohti.

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Fan, Y., Tohti, G., Geni, M. et al. A marigold corolla detection model based on the improved YOLOv7 lightweight. SIViP (2024). https://doi.org/10.1007/s11760-024-03107-2

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  • DOI: https://doi.org/10.1007/s11760-024-03107-2

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