Improved YOLOv8 network using multi-scale feature fusion for detecting small tea shoots in complex environments

Tea shoot segmentation is crucial for the automation of high-quality tea plucking. However, accurate segmentation of tea shoots in unstructured and complex environments presents significant challenges due to the small size of the targets and the similarity in color between the shoots and their background. To address these challenges and achieve accurate recognition of tea shoots in complex settings, an advanced tea shoot segmentation network model is proposed based on You Only Look Once version 8 segmentation (YOLOv8-seg) network model. Firstly, to enhance the model’s segmentation capability for small targets, this study designed a feature fusion network that incorporates shallow, large-scale features extracted by the backbone network. Subsequently, the features extracted at different scales by the backbone network are fused to obtain both global and local features, thereby enhancing the overall information representation capability of the features. Furthermore, the Efficient Channel Attention mechanism was integrated into the feature fusion process and combined with a reparameterization technique to refine and improve the efficiency of the fusion process. Finally, Wise-IoU with a dynamic non-monotonic aggregation mechanism was employed to assign varying gradient gains to anchor boxes of differing qualities. Experimental results demonstrate that the improved network model increases the AP50 of box and mask by 4.33% and 4.55%, respectively, while maintaining a smaller parameter count and reduced computational demand. Compared to other classical segmentation algorithms models, the proposed model excels in tea shoot segmentation. Overall, the advancements proposed in this study effectively segment tea shoots in complex environments, offering significant theoretical and practical contributions to the automated plucking of high-quality tea.