Review of the Current State of Application of Wood Defect Recognition Technology

Wood utilisation is an important factor affecting production costs, but the combined utilisation rate of wood is generally only 50 to 70%. During the production process, the rejection scheme of wood defects is one of the most important factors affecting the wood yield. This paper provides an overview of the main wood defects affecting wood quality, introduces techniques for detecting and identifying wood defects using different technologies, highlights the more widely used image recognition-based wood surface defect identification methods, and presents three advanced wood defect detection and identification equipment. In view of the relatively fixed wood defect recognition requirements in wood processing production, it is proposed that wood defect recognition technology should be further developed toward deep learning to improve the accuracy and efficiency of wood defect recognition.

Automated summary

Wood utilisation is a crucial factor in production costs, with a combined utilisation rate of only 50% to 70%. This paper provides an overview of wood defects affecting quality, introduces various techniques for detecting and identifying defects, particularly focusing on image recognition-based methods, and presents advanced equipment for defect detection. To improve the accuracy and efficiency of wood defect recognition, it is suggested to further develop deep learning technology considering the relatively fixed requirements in wood processing production.