Detection of stem crooks using a CNN trained on synthetic data

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.