High-efficiency tea shoot detection method via a compressed deep learning model
Keywords:
deep learning, tea shoot detection, model compression, high-efficiencyAbstract
Achieving high-efficiency and accurate detection of tea shoots in fields is essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algorithm was proposed in this study. First, tea shoot images were collected and data augmentation was performed to increase sample diversity, and then a spatial pyramid pooling module was added to the YOLOv3 model to detect tea shoots. To simplify the tea shoot detection model and improve the detection speed, the channel pruning algorithm and the layer pruning algorithm were used to compress the model. Finally, the model was fine-tuned to restore its accuracy, and achieve the fast and accurate detection of tea shoots. The test results demonstrated that the number of parameters, model size, and inference time of the tea shoot detection model after compression reduced by 96.82%, 96.81%, and 59.62%, respectively, whereas the mean average precision of the model was only 0.40% lower than that of the original model. In the field test, the compressed model was deployed on a Jetson Xavier NX to conduct the detection of tea shoots. The experimental results demonstrated that the detection speed of the compressed model was 15.9 fps, which was 3.18 times that of the original model. All the results indicate that the proposed method could be deployed on tea harvesting robots with low computing power to achieve high efficiency and accurate detection. Keywords: deep learning, tea shoot detection, model compression, high-efficiency DOI: 10.25165/j.ijabe.20221503.6896 Citation: Li Y T, He L Y, Jia J M, Chen J N, Lyu J, Wu C Y. High-efficiency tea shoot detection method via a compressed deep learning model. Int J Agric & Biol Eng, 2022; 15(3): 159–166.References
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[5] Yuan J. Research process analysis of robotics selective harvesting technologies. Transactions of the CSAM, 2020; 51(9): 1–17. (in Chinese)
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[18] Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C. Learning efficient convolutional networks through network slimming. In: 2017 IEEE International Conference on Computer Vision, Venice, Italy: IEEE, 2017; pp.2755–2763. doi: 10.1109/ICCV.2017.298.
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[26] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA: IEEE, 2016; pp.770–778. doi: 10.1109/CVPR.2016.90.
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Published
2022-06-30
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Li, Y., He, L., jia, J., Chen, J., Lyu, J., & Wu, C. (2022). High-efficiency tea shoot detection method via a compressed deep learning model. International Journal of Agricultural and Biological Engineering, 15(3), 159–166. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6896
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Information Technology, Sensors and Control Systems
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