Accurate tea-shoot detection in real tea gardens is essential for intelligent harvesting, yet dynamic illumination (low light, strong light, and shadows) can cause brightness/contrast fluctuations and feature distribution shifts, degrading detection stability and localization accuracy. This paper proposes LA-YOLO, a dynamic-light tea-shoot detector based on YOLOv11. First, we construct a dynamic-light benchmark dataset and a difficulty-stratified evaluation protocol with four single-light subsets (A–D) and a mixed-light subset (E). Second, we design LA-CSNorm, an input-side brightness-adaptive preprocessing module that applies gated enhancement to dark samples followed by channel-selective normalization to reduce illumination-induced drift. Third, we propose RECA, a residual efficient channel-attention module to enhance discriminative channels and improve localization stability. Ablation studies show that LA-CSNorm and RECA provide complementary gains, and their combination improves the YOLOv11 baseline to 0.831 mAP@0.5 and 0.621 mAP@0.5:0.95, with only 0.01 M additional parameters. On the mixed-light subset E, LA-YOLO achieves 0.816 mAP@0.5 and 0.613 mAP@0.5:0.95, and consistently outperforms mainstream YOLO variants (e.g., YOLOv11m) under dynamic lighting conditions. These results demonstrate that LA-YOLO offers a robust and deployment-friendly solution for tea-shoot detection in complex natural illumination.
Liu et al. (Sat,) studied this question.