Stress Wave Tomography of Wood Internal Defects Based on Deep Learning and Contour Constraint Under Sparse Sampling

In order to detect the size and shape of defects inside wood using stress wave technology under sparse sampling, a novel tomography algorithm is proposed in this paper. The method uses instrument to obtain the stress wave velocity data by sensors hanging around the timber equally, visualizes those data, and reconstructs the image of internal defects with estimated velocity distribution. The basis of the algorithm is using deep learning to assist stress wave tomography to resist signal reduction. First, training CNN model with a large number of generated simulation samples and two-level defect location labeling, and detecting the defective region in wood. Second, using CNN detection results to assist tomography algorithm to precisely estimate the defective area with contour constraint including deepening and weakening operations. Both simulation and wood samples were used to evaluate the proposed method. Effect of CNN detection results on tomography and the shape of the imaging results were both analyzed. The comparison results show that the proposed method always can produce high quality reconstructions with clear edges, when the number of sensors is decreased from 12 to 6.