Pith detection in tree cross-sections is essential for forestry and wood quality analysis but remains a manual, error-prone task. This study evaluates deep learning models -- YOLOv9, U-Net, Swin Transformer, DeepLabV3, and Mask R-CNN -- to automate the process efficiently. A dataset of 582 labeled images was dynamically augmented to improve generalization. Swin Transformer achieved the highest accuracy (0.94), excelling in fine segmentation. YOLOv9 performed well for bounding box detection but struggled with boundary precision. U-Net was effective for structured patterns, while DeepLabV3 captured multi-scale features with slight boundary imprecision. Mask R-CNN initially underperformed due to overlapping detections, but applying Non-Maximum Suppression (NMS) improved its IoU from 0.45 to 0.80. Generalizability was next tested using an oak dataset of 11 images from Oregon State University's Tree Ring Lab. Additionally, for exploratory analysis purposes, an additional dataset of 64 labeled tree cross-sections was used to train the worst-performing model to see if this would improve its performance generalizing to the unseen oak dataset. Key challenges included tensor mismatches and boundary inconsistencies, addressed through hyperparameter tuning and augmentation. Our results highlight deep learning's potential for tree cross-section pith detection, with model choice depending on dataset characteristics and application needs.

翻译：树木横截面髓心检测对于林业和木材质量分析至关重要，但目前仍依赖人工操作且易出错。本研究评估了YOLOv9、U-Net、Swin Transformer、DeepLabV3和Mask R-CNN等深度学习模型，以实现高效自动化检测。通过动态增强582张标注图像的数据集以提升泛化能力。Swin Transformer取得了最高精度（0.94），在精细分割任务中表现突出；YOLOv9在边界框检测中表现良好，但边界精度存在不足；U-Net对结构化纹理效果显著，而DeepLabV3能捕捉多尺度特征但存在轻微边界偏差。Mask R-CNN因重叠检测初始性能不佳，应用非极大值抑制（NMS）后其交并比从0.45提升至0.80。进一步使用俄勒冈州立大学树木年轮实验室提供的11张橡木数据集测试模型泛化性。此外，为探索性分析，采用64张标注树木横截面额外数据集训练性能最差的模型，以验证其对未见橡木数据集的泛化改进效果。研究通过超参数调优和数据增强解决了张量维度不匹配与边界不一致等关键挑战。结果表明深度学习在树木横截面髓心检测中具有显著潜力，模型选择需依据数据集特征与应用需求而定。