Tea Bud Detection based on Improved YOLOv8 in Complex Backgrounds
The detection of tea buds is a prerequisite for achieving intelligent harvesting of high-quality tea.However,in complex tea garden environments,there are problems such as similarity between bud and leaf targets and background,dense growth,which pose difficulties for the accuracy and speed of target detection.To improve the recognition accuracy and processing efficiency of tea buds,a tea bud detection method based on an improved yolov8 model is proposed.This model replaces the standard convolution in the original model with Ghost_Conv,which greatly reduces the parameters of the model;the GAM_Attention module is added after the C2f module in the Backbone to improve the detection ability of tender shoot targets;at the same time,SPPF is applied in front of the Head,which helps the model better integrate the deep semantic information input from the Neck.The experimental results show that the improved model achieves an accuracy(P)of 90.7%,a recall rate(R)of 86%and an average accuracy(mAP)of 92.8%.Compared with the initial network,the improved model has increased P,R,and mAP by 9.6%,7.2%,and 5.7%,respectively.Compared with other detection algorithm models,it has good performance in P,R,mAP,and model complexity,providing theoretical support for the deployment and application of the model in practical scenarios in the later stage.
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