  Tree defect detection is crucial for the structural health screening of
trees. Existing nondestructive testing (NDT) techniques for tree defect
detection require time-consuming and labor-intensive measurement campaigns.
This discourages their application for the routine structural health screening
of whole populations of managed urban trees. To address this issue, this study
proposes a deep-learning augmented stand-off radar scheme for contactless
scanning of tree trunks and rapid detection of tree defects. In this scheme,
the antenna is moved along a straight trajectory at a distance from the tree
trunk to obtain the trunk's B-scan. The obtained raw B-scan is then processed
by a signal-processing framework specifically developed for revealing the
scattering signatures of defects in B-scan, which achieves a 30 dB and 22 dB
increase in the signal-to-clutter and noise ratio of the measurement data of
tree trunk samples and living trees, respectively. Finally, the processed
B-scan is input into a multilevel feature fusion neural network particularly
designed for extracting the signature of the defect in the processed B-scan in
real time. The developed scheme's applications to the detection of defects in
real fresh-cut tree trunks show that the stand-off radar scheme can detect tree
defects with 96% accuracy. This stand-off radar scheme is the first contactless
NDT technique for tree defect detection while operated on a straight trajectory
and potentially can be integrated into the routine tree inspection workflow
which is part of urban tree management.
