Study On The 3D Imaging Technology Of Timber Internal Defect Detection

At present,in the virgin forests with the most timber production value in China,the proportion of rare tree species coverage is very small,and it is susceptible to decay and microbial infestation during the growth period,resulting in the existence of defects such as knots,decay,and cracks within the precious tree species.The demand for precious tree species is even more pressing.In order to get rid of the increase and continuation of the phenomenon of tree species and make up for the shortage of precious tree resources in China,we need to detect and judge whether the internal growth of precious tree species is healthy.The most commonly used method to identify the health of wood is the traditional judgment of flaws by non-destructive testing technology,and through the judgment results,the wood used is selected.However,the traditional non-destructive testing technology has certain destructiveness to the wood itself,the detection speed is slow,and the judgment result also has certain uncertainty.Therefore,it is urgently needed to detect the internal health of the wood in an accurate manner without consuming a lot of time.The high-precision reconstruction prepares for follow-up prevention work,and it is also an indispensable part of the efficient use of wood processing.In this study,computer-based image processing,MATLAB software,and Landweber iteration algorithm for particle swarm optimization were used to perform electrical resistance tomography.The wood holes were used as the research objects,and voltage data acquisition,tomographic image reconstruction,image preprocessing,interpolation processing,and the three-mensional reconstruction and other processes had achieved image reconstruction of common vertical hole defects.This method had certain scientificity and accuracy in the reconstruction of wood hole defects,and had certain research significance in the non-destructive testing of wood defects.In the process of data collection,a hole disc sample wood with a diameter of about 200 mm and a thickness of 500 mm was used as the research object.The PXI platform with electrical signal acquisition and measurement capabilities was used to perform ERT detection of 16-electrode sensors and adjacent excitation modes.Got data and built a finite element model.In the reconstruction of the tomographic image,the particle swarm optimization algorithm was used to optimize the sensitivity matrix and the gain factor was selected.The tomographic defect images were reconstructed by the Landweber iterative algorithm reconstruction formula.In the image preprocessing process,image enhancement of tomographic defect image was performed using histogram equalization,and the traditional median filtering algorithm was improved.Image segmentation was used to separate the wood from the background and separate the wood from the defects,and the contour holes of the wood holes were expanded and filled.Using improved linear interpolation,the interpolation points were classified,the types of interpolation points were judged,and classification inter-layer interpolation was performed for various types of interpolation points.The sequence diagrams and the interpolation images were used,and the volume rendering methods were used to complete the reconstruction of the internal defect image of the wood.