TR-MobileNetV3: A Lightweight Neural Network for Tree-Ring Segmentation

This deposit accompanies the manuscript TR-MobileNetV3: A Lightweight Neural Network for Tree-Ring Segmentation and provides materials related to the mobile deployment of the proposed model. Contents TR-MobileNetV3.rar — Project source code for the proposed TR-MobileNetV3 model, including training/inference scripts, configuration files, and the released model weights. Android application (“HLF Ring Detector”) — A client-side app for offline tree-ring image segmentation based on TR-MobileNetV3. It uses on-device inference (ONNX Runtime for Android), supports capture or gallery input, an adjustable binarisation threshold (0.0–1.0, default 0.5), and export of a segmentation overlay and a binary mask for follow-up analysis (e.g. ring-width or latewood proportion). Minimum platform: Android 7.0 (API 24). Sample images — A compressed archive of representative photographs illustrating typical inputs and use cases discussed in the paper (e.g. standard stem or disc cross-sections, field or museum settings, and more challenging surfaces). These images are for documentation and demonstration only and are not a substitute for the full research image dataset. Scope and limitations The segmentation model was trained primarily on Larix mastersiana latewood masks. Performance on other species or imaging conditions may vary; the images in Sample images are illustrative. For the complete experimental dataset, splits, and quantitative results, see the article and contact the authors if further research data are needed. Suggested citation If you use these materials, please cite the associated publication once available.本数据集存档配套于学术论文《TR-MobileNetV3：一种用于树木年轮分割的轻量级神经网络》，并提供所提出模型的移动端部署相关材料。 ### 数据集内容 1. **TR-MobileNetV3.rar**：所提出TR-MobileNetV3模型的项目源代码包，包含训练/推理脚本、配置文件与已发布的模型权重。 2. **Android应用程序（"HLF Ring Detector"）**：基于TR-MobileNetV3的离线客户端应用，用于树木年轮图像分割。该应用采用端上推理（Android端使用ONNX Runtime），支持相机拍摄或图库选取输入图像，可调节二值化阈值（取值范围0.0–1.0，默认值0.5），并支持导出分割叠加图与二值掩码，用于后续分析（如年轮宽度、晚材占比计算等）。应用最低支持系统版本为Android 7.0（API 24）。 3. **示例图像**：包含代表性照片的压缩归档文件，用于展示论文中讨论的典型输入与应用场景（如标准树干或圆盘横截面、野外或馆藏标本场景，以及更具挑战性的观测表面）。本示例图像仅用于文档说明与演示，不可替代完整的研究图像数据集。 ### 适用范围与局限性 本分割模型主要基于台湾落叶松（Larix mastersiana）晚材掩码进行训练。在其他树种或成像条件下的性能可能存在差异；示例图像中的内容仅为演示用途。如需完整的实验数据集、数据集划分方案与定量分析结果，请参阅本论文；若需进一步的研究数据，可联系论文作者。 ### 引用建议 若您使用本数据集相关材料，请在成果中引用该待正式发表的关联学术论文。