用深度学习提升树轮自动检测，适配多种图像和树种。
DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector
- 用U-Net替代传统边缘检测，实现跨图像域的树轮识别。
- 在松木和皂荚树宏观图像上超越现有方法，柳树显微图像略逊。
- 首次覆盖多物种、多采集方式的自动检测，适合生态与林业研究者。
本文提出Deep CS-TRD，一种用于全截面图像中树轮自动检测的新算法。该方法以深度学习（U-Net）替代原方法中的边缘检测步骤，可适用于显微镜、扫描仪或智能手机拍摄的图像，以及不同树种（火炬松、皂荚树和白柳）。同时，我们向社区公开两个标注图像数据集。在火炬松和皂荚树的宏观图像上，该方法优于现有先进水平；在白柳的显微图像上表现略低。据我们所知，这是首个系统研究此类多物种、多成像条件下的自动树轮检测工作。代码与数据集已发布于https://github.com/hmarichal93/deepcstrd。
原文摘要 · Abstract (English)
Here, we propose Deep CS-TRD, a new automatic algorithm for detecting tree rings in whole cross-sections. It substitutes the edge detection step of CS-TRD by a deep-learning-based approach (U-Net), which allows the application of the method to different image domains: microscopy, scanner or smartphone acquired, and species (Pinus taeda, Gleditsia triachantos and Salix glauca). Additionally, we introduce two publicly available datasets of annotated images to the community. The proposed method outperforms state-of-the-art approaches in macro images (Pinus taeda and Gleditsia triacanthos) while showing slightly lower performance in microscopy images of Salix glauca. To our knowledge, this is the first paper that studies automatic tree ring detection for such different species and acquisition conditions. The dataset and source code are available in https://github.com/hmarichal93/deepcstrd
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