Improving Knot Prediction in Wood Logs with Longitudinal Feature
  Propagation
Salim Khazem Jeremy Fix Cédric Pradalier
The quality of a wood log in the wood industry depends heavily on the
presence of both outer and inner defects, including inner knots that are a
result of the growth of tree branches. Today, locating the inner knots require
the use of expensive equipment such as X-ray scanners. In this paper, we
address the task of predicting the location of inner defects from the outer
shape of the logs. The dataset is built by extracting both the contours and the
knots with X-ray measurements. We propose to solve this binary segmentation
task by leveraging convolutional recurrent neural networks. Once the neural
network is trained, inference can be performed from the outer shape measured
with cheap devices such as laser profilers. We demonstrate the effectiveness of
our approach on fir and spruce tree species and perform ablation on the
recurrence to demonstrate its importance.