A Deep Learning-Augmented Stand-off Radar Scheme for Rapidly Detecting Tree Defects

TL;DR

This paper introduces a novel contactless stand-off radar system augmented with deep learning for rapid, accurate detection of tree defects, significantly improving signal clarity and enabling routine urban tree health assessments.

Contribution

It presents the first contactless NDT method using stand-off radar combined with deep learning for real-time tree defect detection.

Findings

Achieves 96% detection accuracy in real tree trunks.

Enhances signal-to-clutter and noise ratio by 30 dB and 22 dB.

Demonstrates potential for routine urban tree health screening.

Abstract

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.