Articles | Volume 13, issue 1
https://doi.org/10.5194/esurf-13-167-2025
https://doi.org/10.5194/esurf-13-167-2025
Research article
 | 
07 Feb 2025
Research article |  | 07 Feb 2025

Automatic detection of floating instream large wood in videos using deep learning

Janbert Aarnink, Tom Beucler, Marceline Vuaridel, and Virginia Ruiz-Villanueva

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Cited articles

Aarnink, J. and Beucler, T.: Codebase for Automatic Detection of Instream Large Wood in Videos Using Deep Learning, GitHub [code], https://github.com/janbertoo/Instream_Wood_Detection (last access: 1 March 2024), 2024. a
Aarnink, J., Vuaridel, M., and Ruiz-Villanueva, V.: Database for Automatic Detection of Instream Large Wood in Videos Using Deep Learning, Zenodo [data set], https://doi.org/10.5281/zenodo.10822254, 2024. a
Àlex Solé Gómez, Scandolo, L., and Eisemann, E.: A learning approach for river debris detection, Int. J. Appl. Earth Obs., 107, 102682, https://doi.org/10.1016/j.jag.2022.102682, 2022. a
Andreoli, A., Comiti, F., and Lenzi, M. A.: Characteristics, distribution and geomorphic role of large woody debris in a mountain stream of the Chilean Andes, Earth Surf. Proc. Land., 32, 1675–1692, https://doi.org/10.1002/esp.1593, 2007. a
Benda, L. E. and Sias, J. C.: A quantitative framework for evaluating the mass balance of in-stream organic debris, Foreset Ecol. Manag., 172, 1–16, https://doi.org/10.1016/S0378-1127(01)00576-X, 2003. a
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Short summary
This study presents a novel convolutional-neural-network approach for detecting instream large wood in rivers, addressing the need for flexible monitoring methods across diverse data sources. Using a database of 15 228 fully labelled images, the model achieved a weighted mean average precision of 67 %. Fine-tuning parameters and sampling techniques can improve performance by over 10 % in some cases, offering valuable insights into ecosystem management.
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