Lightweight tea bud detection method based on improved YOLOv5

Abstract

Tea bud detection technology is of great significance in realizing automated and intelligent plucking of tea buds. This study proposes a lightweight tea bud identification model based on modified Yolov5 to increase the picking accuracy and labor efficiency of intelligent tea bud picking while lowering the deployment pressure of mobile terminals. The following methods are used to make improvements: the backbone network CSPDarknet-53 of YOLOv5 is replaced with the EfficientNetV2 feature extraction network to reduce the number of parameters and floating-point operations of the model; the neck network of YOLOv5, the Ghost module is introduced to construct the ghost convolution and C3ghost module to further reduce the number of parameters and floating-point operations of the model; replacing the upsampling module of the neck network with the CARAFE upsampling module can aggregate the contextual tea bud feature information within a larger sensory field and improve the mean average precision of the model in detecting tea buds. The results show that the improved tea bud detection model has a mean average precision of 85.79%, only 4.14 M parameters, and only 5.02G of floating-point operations. The number of parameters and floating-point operations is reduced by 40.94% and 68.15%, respectively, when compared to the original Yolov5 model, but the mean average precision is raised by 1.67% points. The advantages of this paper’s algorithm in tea shot detection can be noticed by comparing it to other YOLO series detection algorithms. The improved YOLOv5 algorithm in this paper can effectively detect tea buds based on lightweight, and provide corresponding theoretical research for intelligent tea-picking robots.