基于改进YOLOv5s的碾茶轻量化在线分选方法

Abstract

Abstract: Milled tea is the raw material for the production of matcha and it is the most important factor in ensuring the quality of matcha. Rapid and effective sorting of milled tea improves its quality. Due to the low efficiency and high labor intensity of the current sorting process in milled tea production, an online sorting system for milled tea was developed in this study. This system is composed of a material conveying system, an image acquisition system, an image recognition system, a positioning system and a sorting execution control system. The image acquisition system is used to collect the milled tea image and make the milled tea image data set. According to the real-time and lightweight requirements of milled tea recognition, the EfficientNet backbone network and SimSPPF module were introduced based on the YOLOv5s model, and the YOLOv5s-EfficientNet-SimSPPF model was improved and designed. On the basis of ensuring the recognition precision, the model recognition speed was improved and the model size was reduced. The established test set was used to evaluate the recognition performance of YOLOv5s, YOLOv5s-EfficientNet, YOLOv5s-SimSPPF and YOLOv5s-EfficientNet-SimSPPF on the PC. The recognition precision, recall, mAP@0.5, inference time and model size of YOLOv5s-EfficientNet-SimSPPF were 0.993, 0.981, 0.995, 6.3 ms and 2.85 MB, respectively. This study also proposed a sorting control algorithm for online sorting based on the online recognition results of milled tea. In addition, an auxiliary algorithm was proposed to prevent low-precision secondary recognition and secondary positioning of the milled tea on the boundary of the field of view of the industrial camera during the sorting process. The YOLOv5s-EfficientNet-SimSPPF model was deployed to the edge device Jetson Nano B01, and the model was tested using the test set. The recognition precision and speed were 0.982 ms and 37.5 ms, respectively. The results show that the real-time milled tea recognition can essentially be achieved by deploying the model migration to the developed online milled tea sorting system. Finally, the milled tea separation was carried out on the designed and developed platform, and the average separation accuracy rate of mixed milled tea leaves and milled tea stems reached 97.0%. The online milled tea sorting system proposed in this paper can meet the actual needs of online milled tea sorting, and can be used as an effective tool for the fine processing of matcha and milled tea sorting operation, providing a reference for online recognition and continuous sorting of other agricultural products.

Key words: milled tea recognition, improved YOLOv5s algorithm, online sorting system

CLC Number:

LI Bing, ZHU Yong, XIA Chenglong, LI Feilong, CAI Zhenyang, WU Hao. Lightweight Online Sorting Method of Milled Tea Based on Improved YOLOv5s[J]. Journal of Tea Science, 2025, 45(5): 879-897.

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Lightweight Online Sorting Method of Milled Tea Based on Improved YOLOv5s

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