Dual-Branch Multi-Scale Wavelet Enhancement Method for Tea Bud Detection

Tea bud detection is a critical technology in the development of intelligent tea-picking robots. To address the issues of high false negative and false positive rates under complex environment as well as poor generalization performance across varieties, weather conditions, years, etc., this study proposes a method named DMWE-YOLOv8n based on dual-branch multi-scale wavelet enhancement for tea bud detection. In this method, a multi-scale wavelet feature branch is incorporated in parallel into the model’s backbone and multi-level wavelet features are extracted through two-dimensional discrete wavelet transform, followed by dynamic wavelet feature enhancement. Then, the spatial features and wavelet features with the same scale are then enhanced by a gated attention mechanism and fused in a bidirectional guided manner. Finally, a novel wavelet cosine loss is introduced to boost the model’s learning capability and generalization. Experimental results demonstrate improved performance of DMWE-YOLOv8n on the ZC108-23 tea bud dataset and five cross-domain tea bud datasets. Specifically, in terms of AP50, it outperforms the similarly sized YOLOv11n by 1.6% on ZC108-23 and by 7.65%, 9.83%, 10.67%, 5.15%, and 6.80% on the five cross-domain datasets, respectively. In conclusion, the proposed method achieves an excellent balance between detection accuracy, computational cost, and generalization performance.