Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation
This work addresses a practical problem for the wood industry by enabling cheaper defect detection, but it is incremental as it applies existing neural network techniques to a specific domain.
The paper tackled predicting inner knot locations in wood logs from outer shape data to reduce reliance on expensive X-ray scanners, achieving effective results on fir and spruce species with ablation studies showing the importance of recurrence in their method.
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.