Papers › DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector
DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector
Henry Marichal, Verónica Casaravilla, Candice Power, Karolain Mello, Joaquín Mazarino, Christine Lucas, Ludmila Profumo, Diego Passarella, Gregory Randall
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 this https URL
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