碾茶是生产抹茶的原料,是确保抹茶品质最重要的一环。对碾茶进行快速有效分选是提高成品碾茶品质的重要环节。针对目前碾茶分选环节中存在分选效率低、劳动强度大等问题,提出了一种基于碾茶检测的在线分选方法,搭建了碾茶在线分选试验平台。根据碾茶识别的实时性和轻量化需求,开发了一种基于YOLOv5s-EfficientNet-SimSPPF的碾茶识别模型,模型引入EfficientNet主干网络和SimSPPF模块,在保证识别精度的基础上提高了模型识别速度,缩小模型大小。该研究还提出了一种基于碾茶在线识别结果进行分选的控制算法,以及一种辅助算法,防止分选过程中对工业相机视场域边界上的碾茶进行低精确度的二次识别与二次定位。最后,设计了碾茶在线分选试验,对混合碾茶叶片和梗茎的样品分选平均精度达到97.0%。提出的碾茶在线分选方法可以满足碾茶在线分选的实际需要,可作为抹茶精加工和碾茶分选操作的有效工具,也可为其他农产品的在线识别和连续分选提供参考。
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.
关键词
改进YOLOv5s算法 /
碾茶识别 /
在线分选系统
Key words
improved YOLOv5s algorithm /
milled tea recognition /
online sorting system
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基金
安徽省高等学校科学研究项目（2022AH040124）、安徽省科技厅科技攻关项目（2022296906020005）
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