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

• DOI: 10.48550/arxiv.2410.11913
• Publication Date: 2024-10-15
• Authors: Rijun Wang; Guanghao Zhang; Hongyang Chen; Xinye Yu; Yesheng Chen; Fulong Liang; Xiangwei Mou; Bo Wang
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition

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