A lightweight tea buds terminal detection model based on YOLOv5s
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 em-bedded 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 abil-ity for tea buds is enhanced.Next,multi-scale context(MSC)module is introduced into the Neck network to effec-tively fuse shallow image features and deep semantic features,which helps the network model extract effective recog-nition 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 experi-ment 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 trans-formation.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 pick-ing of tea buds.
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