Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning

TL;DR

This paper presents a deep learning-based wood panel bark removal system with a vision inspection component, demonstrating improved quality and efficiency through the use of the BiSeNetV1 segmentation model tested in real-world sawmill environments.

Contribution

It introduces a novel deep learning approach with a custom semantic segmentation dataset for bark removal, enhancing process accuracy and efficiency in sawmill operations.

Findings

BiSeNetV1 model effectively segments wood panels for bark removal

The equipment significantly improves bark removal quality

The system meets sawmill requirements for precision and efficiency

Abstract

Attempting to apply deep learning methods to wood panels bark removal equipment to enhance the quality and efficiency of bark removal is a significant and challenging endeavor. This study develops and tests a deep learning-based wood panels bark removal equipment. In accordance with the practical requirements of sawmills, a wood panels bark removal equipment equipped with a vision inspection system is designed. Based on a substantial collection of wood panel images obtained using the visual inspection system, the first general wood panels semantic segmentation dataset is constructed for training the BiSeNetV1 model employed in this study. Furthermore, the calculation methods and processes for the essential key data required in the bark removal process are presented in detail. Comparative experiments of the BiSeNetV1 model and tests of bark removal effectiveness are conducted in both laboratory and sawmill environments. The results of the comparative experiments indicate that the application of the BiSeNetV1 segmentation model is rational and feasible. The results of the bark removal effectiveness tests demonstrate a significant improvement in both the quality and efficiency of bark removal. The developed equipment fully meets the sawmill's requirements for precision and efficiency in bark removal processing.