Classification recognition of tea buds based on YOLOv7 optimization model
Tea bud classification and identification is an important part of the realization of automatic tea classifier.In order to achieve accurate identification of high-quality tea shoots,a tea bud classification method based on improved YOLOv7 was proposed.Firstly,CBAM-ELAN module is constructed based on the CBAM attention mechanism integrated into the backbone network.Under the synergistic effect of channel and spatial attention module,the weight of background part is reduced and the feature extraction ability of bud is enhanced.Receptive field enhancement(RFE)module and explicit visual center(EVC)module on the Neck side are introduced to generate a new receptive field relationship.The ability to adjust the features in the layer was improved and the ability to extract bud features was enhanced.In this paper,the detection accuracy of the improved YOLOv7 algorithm for tea leaves is 89.3%for single bud,88.9%for one bud and one leaf,and 95.7%for one bud and two leaves.Compared with the original YOLOv7,the accuracy rate is increased by 1.3,1.3 and 1.5 percentage points respectively.End-to-end target detection and different posture recognition of high-quality tea seedlings were realized,which could provide an important theoretical basis for tea bud classification and recognition.
万方数据
维普
