Precise Stress Wave Imaging Of Internal Defects In Trunks

During the growth of trees,internal defects such as knots,holes,decay and cracks may occur due to various reasons.These defects are difficult to be detected in trunks and will seriously harm the health of trees.How to detect the internal defects of trunks from the outside and image them has important research significance,which can effectively protect the health of trees.Stress wave technology has become the mainstream technology in this imaging field because of its low cost,good portability,easy to use,unaffected by the size of trunks,no damage to trees,and no harm to human health.Although the stress wave imaging method has made some progress in the world and can directly reflect the location,contour and severity of the defects in trunks by reconstructing images,the research on this method is still immature.At present,there are four main problems.Firstly,the observed signal of stress wave method is often insufficient,and the existing tomography algorithms can not reconstruct high-quality defect images,which leads to the low accuracy of tomography algorithms in this field.Secondly,it is time-consuming and laborious to place sensors on the surface of trunks.In practical application,when the number of sensors is reduced,the imaging accuracy can not be guaranteed.Thirdly,how to automatically determine the optimal sensor placement strategy for precious trees such as ancient and famous trees to achieve high-precision stress wave imaging.Fourthly,the existing stress wave imaging methods focus on tomography,how to realize the three-dimensional stress wave imaging of the internal defects in trunks and reconstruct the complete spatial distribution of the defects.In this paper,the stress wave tomography method for internal defects in trunks,the stress wave tomography method under sparse sampling,the optimization of sensor spatial layout,and the three-dimensional stress wave imaging method are studied.The purpose of this research is to promote the development of stress wave tomography from "rough" to "precise".The main innovative research is as follows,(1)Aiming at the anisotropy of wood,signal modification was carried out based on the propagation law of stress wave in the cross section of trunks to solve the problem of uneven wave velocity in the cross section of trunks.Then,a novel stress wave tomography algorithmbased on weighted ellipse space interpolation is proposed to estimate the wave velocity distribution of the mesh element by establishing the ellipse influence region with different curvature according to the ray length.The experimental results show that the method can deal with complex samples with double defects and can also resist the signal interference caused by the change of defect area density.Then,a novel stress wave tomography algorithm based on ray segmentation and segmented ellipse interpolation is also proposed.The original ray is segmented through the ellipse influence region to obtain the improved propagation ray graph.Then the ellipse influence region is established to estimate the wave velocity of the mesh element.The experimental results show that the average imaging accuracy of this method is 86.9%,showing the high quality of the reconstructed image.(2)Aiming at the problem of maintaining the accuracy of stress wave imaging under sparse sampling,two algorithms are proposed.First,a novel stress wave hybrid tomography algorithm based on compressed sensing and ellipse interpolation is proposed.The inherent spatial structure of the defect area is reconstructed by the advantage of compressed sensing in the sparse signal representation and solution of stress wave,and the non-defect area is reconstructed by spatial interpolation method.The feature points are selected,mixed and combined imaging is performed.Experimental results show that the algorithm can still achieve high quality tomography results under sparse sampling.Then,a novel stress wave tomography algorithm based on deep learning and contour constraint is further proposed.Convolutional Neural Network is used to study the rays distribution in the ray graph and detect the defective object,which helps the imaging algorithm to constrain the contour of the defective area.The experimental results show that the overall imaging accuracy of this method is 95.1%,and the imaging effect is not affected greatly when the number of sensors is reduced.(3)Aiming at the problem of sensor spatial layout optimization,the effects of stress wave ray traversal ratio and uniformity of sensor arrangement on imaging are analyzed through experiments.Then,a novel optimization algorithm of stress wave sensor spatial layout based on defect distribution pattern perception is proposed.The relationship between the defect spatial distribution pattern and the sensor spatial layout is established by using the ray traversal ratio and the uniformity of sensor layout.The optimal sensor spatial layout is searched by optimizing the conditions and constructing the fitness function.The contrast experiment results show that after the optimization of sensor placement,the imaging accuracy can be further increased by 5.8%.(4)Aiming at the problem of three-dimensional stress wave imaging,the three-dimensional signal of stress wave in wood was collected firstly,and the wave velocity signal was corrected based on the propagation law of stress wave in the longitudinal and transverse sections of trunks.Then,a novel 3-D imaging algorithm based on Kriging space interpolation is proposed to buildthe structure relationship between interpolation points and reference points in 3-D space,and the accurate reference points are extracted as the input data of the spatial interpolation algorithm.Finally,the visualization of the 3-D image of defects is realized.The experimental results show that the average imaging accuracy of this method is 89.1%,which shows the effectiveness of the algorithm.