基于YOLOv5s的轻量级茶叶嫩芽终端检测模型

A lightweight tea buds terminal detection model based on YOLOv5s

在茶园环境中快速精准识别茶叶嫩芽是实现智能化采茶的关键技术之一,但茶芽检测模型的复杂性导致模型参数量大、计算量大、模型尺寸大,限制了模型在采茶机器人嵌入式设备的部署。鉴于此,本文提出一种基于YOLOv5s的轻量级茶叶嫩芽终端检测模型。首先,使用轻量级网络GhostNet替换YOLOv5s中的Backbone网络,并重构Neck网络,降低模型的参数量、计算量和内存占用量,改进后的模型分别降低了47.64%、49.36%、45.51%。其次,通过引入协调注意力(coordinate attention,CA)机制,抑制图像背景信息,增强模型对茶叶嫩芽的特征提取能力。接着,在Neck网络引入多尺度特征融合(multi-scale context,MSC)模块,有效融合浅层图像特征和深层语义特征,帮助网络模型提取有效识别信息。最后,使用边界框回归损失函数EIOU替换CIOU,加快损失函数收敛速度,提高茶叶嫩芽边界框定位精度。试验结果表明,与原YOLOv5s模型相比,改进模型的参数量、计算量以及模型内存占用量分别降低了3 Mb、7.3 Gb和6.37 Mb,检测精度提升0.3%。通过模型转换将该模型移植到树莓派平台,经过环境部署和推理引擎加速,达到了轻量级模型在资源和算力有限的树莓派上对茶叶嫩芽检测的目的,在一定程度上提高了茶叶嫩芽的识别精确度,为茶叶嫩芽的智能化采摘提供了理论研究和技术支持。

Rapid and accurate identification of tea buds in tea garden environments is one of the key technologies for achieving intelligent tea picking.However,the complexity of the tea buds detection model leads to problems such as large model parameters,computational complexity,and model size,which limits the deployment of this model in embedded devices of tea picking robots.In view of this,this article proposes a lightweight tea buds terminal detection model based on YOLOv5s.Firstly,the lightweight network GhostNet is used to replace the Backbone network in YOLOv5s,and the Neck network is reconstructed to reduce the parameters,computation and memory consumption of the model.The improved model reduces 47.64%,49.36%and 45.51%respectively.Secondly,by introducing a coordinated attention(CA)mechanism to suppress image background information,the model s feature extraction ability for tea buds is enhanced.Next,multi-scale context(MSC)module is introduced into the Neck network to effectively fuse shallow image features and deep semantic features,which helps the network model extract effective recognition information.Then,the boundary box regression Loss function CIOU is replaced by EIOU to accelerate the Rate of convergence of the Loss function and improve the positioning accuracy of the tea buds boundary box.The experiment result shows that compared with the original YOLOv5s model,the improved model reduces the parameter count,computational complexity,and model memory usage by 3 Mb,7.3 Gb,and 6.37 Mb,respectively,and improves detection accuracy by 0.3%.Finally,the model was transplanted to the Raspberry Pi platform through model transformation.After environmental deployment and inference engine acceleration,the lightweight model achieved the goal of detecting tea buds on Raspberry Pi with limited resources and computing power.It also improved the recognition accuracy of tea buds to a certain extent,providing theoretical research and technical support for the intelligent picking of tea buds.