Detection of Knots in Oak Wood Planks: Instance versus Semantic segmentation

In this study, we present a new dataset of knotcovered oak planks. It contains 1500 images that have 1 to 11 knots per image, along with mask and bounding-box annotations. The data set was evaluated using deep machine learning methods, and it has been found that instance segmentation models are superior in this task, achieving 59% Box-IoU versus 49% Box-IoU using semantic segmentation. Instance segmentation performed better to detect knots by segmenting instances with an accuracy of 90%, while semantic segmentation detected konts with an accuracy of 89%.