A LIGHTWEIGHT TEA BUD DETECTION METHOD FOR ONLINE GRADING OF FRESH TEA LEAVES BASED ON IMPROVED YOLOV11N

Abstract

To address the limitations of computational resources and the stringent real-time stability requirements in post-harvest online grading of fresh tea leaves, this study proposes a lightweight object detection method oriented toward efficient deployment. The YOLOv11n network was selected as the baseline model. A StarNet backbone was introduced to enhance nonlinear inter-channel interactions while reducing the complexity of feature extraction. In addition, a DySample dynamic upsampling module was employed to predict content-adaptive sampling locations, thereby improving multi-scale feature reconstruction under lightweight constraints. Furthermore, a wavelet-based pooling structure was designed to perform structure-aware frequency-domain decomposition, preserving critical edge and texture information while reducing redundant computation. The Inner-MPDIoU loss function was also adopted to calculate overlap consistency within a compact core region, thereby improving bounding-box localization stability for fine-grained structures. Based on these integrated improvements, the lightweight YOLOv11-SDWI detection model was developed. Experimental results demonstrated that the proposed model achieved an accuracy of 89.5%, with only 4.9 GFLOPs and 1.89 M parameters. To further verify its engineering applicability, the algorithm was encapsulated into a complete visual software system specifically designed for fresh tea leaf sorting. The developed system provides a functional interface for real-time detection, effectively bridging theoretical algorithm design and practical agricultural applications, and establishing a solid software foundation for future deployment on physical sorting equipment.