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Journal of Tea Science ›› 2022, Vol. 42 ›› Issue (4): 549-560.

• Research Paper •

### Tea Buds Detection Model Using Improved YOLOv4-tiny

FANG Mengrui1, LÜ Jun1,*, RUAN Jianyun2, BIAN Lei2, WU Chuanyu3, YAO Qing1

1. 1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;
2. Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China;
3. School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
• Received:2022-05-09 Revised:2022-06-09 Online:2022-08-15 Published:2022-08-23

Abstract: Precise detection of tea buds is a prerequisite for intelligent mechanical picking of tea. Aiming at the problems of poor salience and high missed detection rate of small-scale buds caused by different sizes of tea leaves and the cover of other tea leaves, this paper proposed a kind of tea buds detection model based on improved YOLOv4-tiny. In this model, a 52×52 shallow feature layer was added in the neck network to promote the attention of YOLOv4-tiny network to small target buds. A convolutional block attention module (CBAM) was introduced to suppress the background noise and improve the salience of buds, and a bidirectional feature pyramid network (BiFPN) was used to integrate characteristic information of different scales, so as to propose the YOLOv4-tiny-Tea, a high performance light weight tea buds detection model. The results of model training and performance testing on the same training set and test set show that for the YOLOv4-tiny-Tea model, the detection precision and recall rate were 97.77% and 95.23% respectively, which were 5.58% and 23.14% higher than those before modification. An ablation experiment verified the effectiveness of the modified network structure in detecting different scales of buds, and a comparison of YOLOv4-tiny-Tea model with three YOLO algorithms found that the F1 value of YOLOv4-tiny-Tea model was 12.11%, 11.66% and 6.76% higher than F1 values of YOLOv3, YOLOv4 and YOLOv5l models respectively. The number of parameters in YOLOv4-tiny-Tea model was merely 13.57%, 13.06% and 35.05% of the three network models. The experimental results demonstrate that the method proposed in this paper effectively improved the detection precision of buds under different scales, greatly reduced the missed detection rate of buds for small size or under shading, and significantly bettered the detection precision based on a lightweight computation overhead. Therefore, the method can meet the needs of agricultural robots for real-time detection and embedded development, thus providing a reference for intelligent tea buds picking.

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