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DeepCS-TRD, a Deep Learning-Based Cross-Section Tree Ring Detector

Title: DeepCS-TRD, a Deep Learning-Based Cross-Section Tree Ring Detector
Authors: Marichal, Henry; Casaravilla, Verónica; Power, Candice; Mello, Karolain; Mazarino, Joaquín; Lucas, Christine; Profumo, Ludmila; Passarella, Diego; Randall, Gregory
Contributors: Rodolà, Emanuele; Galasso, Fabio; Masi, Iacopo
Source: Marichal, H, Casaravilla, V, Power, C, Mello, K, Mazarino, J, Lucas, C, Profumo, L, Passarella, D & Randall, G 2026, DeepCS-TRD, a Deep Learning-Based Cross-Section Tree Ring Detector. in E Rodolà, F Galasso & I Masi (eds), Image Analysis and Processing – ICIAP 2025 - 23rd International Conference, Proceedings. Springer Science+Business Media, Lecture Notes in Computer Science, vol. 16167 LNCS, pp. 29-41, 23rd International Conference on Image Analysis and Processing, ICIAP 2025, Rome, Italy, 15/09/2025. https://doi.org/10.1007/978-3-032-10185-3_3
Publisher Information: Springer Science+Business Media
Publication Year: 2026
Collection: Aarhus University: Research
Subject Terms: Deep learning; Dendrochronology; Tree rings detection; U-Net
Description: 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://hmarichal93.github.io/deepcstrd/.
Document Type: conference object
Language: English
ISBN: 978-3-032-10184-6; 3-032-10184-0
ISSN: 97830321
Relation: info:eu-repo/semantics/altIdentifier/isbn/9783032101846; urn:ISBN:9783032101846
DOI: 10.1007/978-3-032-10185-3_3
Availability: https://pure.au.dk/portal/en/publications/d25bc7df-5a70-4f97-9670-97115cf1663a; https://doi.org/10.1007/978-3-032-10185-3_3; https://www.scopus.com/pages/publications/105027592367
Rights: info:eu-repo/semantics/restrictedAccess
Accession Number: edsbas.D3BBF84B
Database: BASE