Confidence-based stress wave tomography for detecting internal defects of trees

Internal defects in trees, such as cavities and decay, have threatened the safety of heritage trees, due mainly to the mechanical stability and economic value of trees. However, the conventional detection of the internal defects in trees can rely mainly on destructive sampling or low-resolution imaging techniques. Both high accuracy and non-invasiveness are often required in the forestry resource protection and health assessment of heritage trees. Meanwhile, the existing stress wave tomography cannot consider the spatial variability in the velocity information, resulting in some discrepancies between the reconstructed images and the actual defect distribution. In this study, a high-precision tomographic imaging algorithm was proposed to detect the internal defects in trees. The CTIA (confidence tomographic imaging algorithm) integrated the elliptical spatial interpolation into the stress wave propagation paths as elliptical influence zones. Grid velocities were calculated to weight the average ray data within elliptical influence zones. Among them, the confidence metrics were derived from elliptical coverage density, local velocity properties, and geometric configurations. Spatial consistency between adjacent grids was enforced to estimate the iterative neighborhood optimization refined velocity. A weighting scheme was also balanced to combine the current grid confidence with neighborhood velocity standard deviation. To enhance interpretability, A trichromatic visualization scheme was adopted to distinguish healthy tissues, transitional decay areas, and cavities, using normalized velocity thresholds as the classification criteria. In addition, a tradeoff between computational complexity and imaging accuracy was achieved for the stable convergence and reliable reconstruction under varying experimental conditions. Experimental validation was conducted on five log samples, including Ginkgo biloba, Sapium sebiferum, Cinnamomum camphora, Cedrus, and Carya cathayensis, with the moisture contents ranging from 10.5% to 18.3% and defect area ratios from 2.97% to 26.84%. Four simulation samples contained circular defects with the area ratios from 3.08% to 16.21%. Quantitative analysis revealed that there was the CTIA's superiority over EBSI (ellipse-based spatial interpolation) and Intersection IFDD (intersection fitting-based defect detection in woods), with 77.24% for average recall, 81.72% for precision, 79.04% for F1 score, and 70.49% for IOU. Compared with the EBSI, the CTIA improved the average F1 score by 8.69 percentage points and the average IOU by 14.00 percentage points, respectively. Compared with the IFDD, the CTIA was improved by approximately 7.20 percentage points in the precision, indicating the high accuracy and robustness. The absolute relative error in the predicted defect area proportion was only 1.87% among all tests, compared with 2.65% for IFDD and 6.07% for EBSI, demonstrating the enhanced accuracy of CTIA in defect area estimation. A more stable and reliable reconstruction was obtained over different tree species and defect morphologies, indicating its generalization. The irregular defects were also reconstructed in the multi-defect scenarios. Nevertheless, its detection performance declined significantly as the boundary adhesion occurred. Consequently, the internal structures of trees were non-destructively evaluated to dynamically balance the velocity reliability and spatial consistency. A practical tool can offer for the forestry and heritage tree preservation, as well as the ecological and economic challenges in sustainable resource utilization. Adaptive confidence allocation or deep-learning approaches can be incorporated to further improve the performance in complex and multi-defect conditions.