Intelligent detection of wood defects based on 3D scanning technology

Wood defect detection is a necessary process and link for efficient use of wood processing, which is of great significance for improving wood quality and enhancing the economic benefits of wood processing. Traditional detection techniques have disadvantages such as destructiveness, slow detection speed, and low accuracy. In this study, a single segment object detection algorithm was used to identify and locate surface defects on wood by combining 3D scanning technology and deep learning algorithms. The non-destructive testing of wood was carried out using 3D laser scanning technology. Firstly, the surface feature data of the wood was extracted using a 3D laser scanner, and a preliminary 3D model image of the wood was constructed; 3D reconstruction was performed using Geomagic Control software, and the surface of the wood was segmented using the triangular mesh algorithm; Based on the YOLOv5 network in deep learning, the point cloud data of wood is trained to obtain the surface defect area of wood and achieve wood defect detection; The research results indicate that the combination of 3D scanning technology and deep learning algorithms for wood surface defect detection can achieve efficient and accurate detection, effectively improving the intelligence level and production efficiency of wood processing.