YOLO11融合注意力模块，全年精准分割果园树干树枝
Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards
- 将CBAM注意力模块嵌入YOLO11，提升多季节特征捕捉能力
- 树干类精度达0.83，枝条类达0.75，比无CBAM提升明显
- 适用于果树全周期监测，适合智慧农业场景
本研究通过将卷积块注意力模块（CBAM）与YOLO11架构结合，构建了定制化的实例分割模型。该模型在休眠季与生长期苹果园图像混合数据集上训练，旨在提升全年不同季节条件下树干与枝条的分割性能。模型在休眠季和生长期图像上分别进行独立验证，并进一步测试了萌芽前、开花期、疏果期及采收期的表现。结果显示，YOLO11x-seg-CBAM在休眠季精度最高，达0.91；而YOLO11m-seg-CBAM在树干类达到0.83的最高精度，枝条类达0.75，显著优于无CBAM的基线模型（分别为0.80和0.73）。在生长期，YOLO11s-seg在所有类别中表现最优，枝条精度0.516，树干精度0.64。该方法证明了基于双季节数据训练的YOLO11-CBAM在全年复杂环境下对果树结构的高效分割潜力。
原文摘要 · Abstract (English)
In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, trained on a mixed dataset of dormant and canopy season apple orchard images, aimed to enhance the segmentation of tree trunks and branches under varying seasonal conditions throughout the year. The model was individually validated across dormant and canopy season images after training the YOLO11-CBAM on the mixed dataset collected over the two seasons. Additional testing of the model during pre-bloom, flower bloom, fruit thinning, and harvest season was performed. The highest recall and precision metrics were observed in the YOLO11x-seg-CBAM and YOLO11m-seg-CBAM respectively. Particularly, YOLO11m-seg with CBAM showed the highest precision of 0.83 as performed for the Trunk class in training, while without the CBAM, YOLO11m-seg achieved 0.80 precision score for the Trunk class. Likewise, for branch class, YOLO11m-seg with CBAM achieved the highest precision score value of 0.75 while without the CBAM, the YOLO11m-seg achieved a precision of 0.73. For dormant season validation, YOLO11x-seg exhibited the highest precision at 0.91. Canopy season validation highlighted YOLO11s-seg with superior precision across all classes, achieving 0.516 for Branch, and 0.64 for Trunk. The modeling approach, trained on two season datasets as dormant and canopy season images, demonstrated the potential of the YOLO11-CBAM integration to effectively detect and segment tree trunks and branches year-round across all seasonal variations. Keywords: YOLOv11, YOLOv11 Tree Detection, YOLOv11 Branch Detection and Segmentation, Machine Vision, Deep Learning, Machine Learning
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