Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation

TL;DR

This paper introduces a neural network-based method to predict inner knots in wood logs from outer shape data, reducing reliance on expensive X-ray equipment and enabling cost-effective quality assessment.

Contribution

The paper presents a novel convolutional recurrent neural network approach for predicting inner defects from outer log contours, demonstrating effectiveness on fir and spruce species.

Findings

Effective knot prediction from outer shape data

Reduces need for expensive X-ray scanners

Demonstrates success on multiple tree species

Abstract

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.