Li Hongfei, Kong Min, Shi Yun
School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China.
School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.
Biomimetics (Basel). 2024 Nov 13;9(11):692. doi: 10.3390/biomimetics9110692.
The detection of tea bud targets is the foundation of automated picking of premium tea. This article proposes a high-performance tea bud detection model to address issues such as complex environments, small target tea buds, and blurry device focus in tea bud detection. During the spring tea-picking stage, we collect tea bud images from mountainous tea gardens and annotate them. YOLOv5 tea is an improvement based on YOLOv5, which uses the efficient Simplified Spatial Pyramid Pooling Fast (SimSPPF) in the backbone for easy deployment on tea bud-picking equipment. The neck network adopts the Bidirectional Feature Pyramid Network (BiFPN) structure. It fully integrates deep and shallow feature information, achieving the effect of fusing features at different scales and improving the detection accuracy of focused fuzzy tea buds. It replaces the independent CBS convolution module in traditional neck networks with Omni-Dimensional Dynamic Convolution (ODConv), processing different weights from spatial size, input channel, output channel, and convolution kernel to improve the detection of small targets and occluded tea buds. The experimental results show that the improved model has improved , , and mean average by 4.4%, 2.3%, and 3.2%, respectively, compared to the initial model, and the inference speed of the model has also been improved. This study has theoretical and practical significance for tea bud harvesting in complex environments.
茶芽目标检测是优质茶叶自动化采摘的基础。本文提出了一种高性能茶芽检测模型，以解决茶芽检测中复杂环境、小目标茶芽以及设备聚焦模糊等问题。在春茶采摘阶段，我们从山区茶园采集茶芽图像并进行标注。YOLOv5 tea是基于YOLOv5的改进版本，其主干网络采用高效的简化空间金字塔池化快速版（SimSPPF），便于在茶芽采摘设备上部署。颈部网络采用双向特征金字塔网络（BiFPN）结构。它充分整合了深浅层特征信息，实现了不同尺度特征融合的效果，提高了聚焦模糊茶芽的检测精度。它用全维动态卷积（ODConv）取代了传统颈部网络中的独立CBS卷积模块，对来自空间大小、输入通道、输出通道和卷积核的不同权重进行处理，以提高对小目标和被遮挡茶芽的检测能力。实验结果表明，与初始模型相比，改进后的模型的精确率、召回率和平均精度均值分别提高了4.4%、2.3%和3.2%，并且模型的推理速度也得到了提升。本研究对于复杂环境下的茶芽采摘具有理论和实际意义。
