de Paula Pires, Raul
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
Abstract
Defects like stem crooks significantly impact the value and usability of wood. Obtaining sufficient training data for models that detect such irregularities can be challenging. Thus, data simulation offers a promising solution to overcome data scarcity. By generating annotated datasets, we can train models for detecting defects in standing trees.
Keywords
Stem crooks; synthetic data; 3D simulations; Terrestrial laser scanning; deep learning
Published in
Publisher: Silvilaser
Conference
SilviLaser 2025, September 29 - October 3, 2025, Québec City, Quebec, Canada
SLU Authors
Persson, Henrik
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
Holmgren, Johan
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
UKÄ Subject classification
Forest Science
Permanent link to this page (URI)
https://res.slu.se/id/publ/145013