Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing

Feng, Jinglun; Yang, Liang; Hoxha, Ejup; Xiao, Jizhong

doi:10.1109/jsen.2022.3164707

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…In particular, the characteristics of various cavities in GPR B-scan images tended to be similar. Therefore, to improve the classification performance, both the GPR B-scan and C-scan images were considered in the classification process using the DL network [14][15][16][17]. Compared with the 2D GPR data, 3D data can provide rich spatial information and greatly improve the process in terms of data volume, imaging methods, and disease detection accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

DL-Aided Underground Cavity Morphology Recognition Based on 3D GPR Data

et al. 2022

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Cavity under urban roads has increasingly become a huge threat to traffic safety. This paper aims to study cavity morphology characteristics and proposes a deep learning (DL)-based morphology classification method using the 3D ground-penetrating radar (GPR) data. Fine-tuning technology in DL can be used in some cases with relatively few samples, but in the case of only one or very few samples, there will still be overfitting problems. To address this issue, a simple and general framework, few-shot learning (FSL), is first employed for the cavity classification tasks, based on which a classifier learns to identify new classes given only very few examples. We adopt a relation network (RelationNet) as the FSL framework, which consists of an embedding module and a relation module. Furthermore, the proposed method is simpler and faster because it does not require pre-training or fine-tuning. The experimental results are validated using the 3D GPR road modeling data obtained from the gprMax3D system. The proposed method is compared with other FSL networks such as ProtoNet, R2D2, and BaseLine relative to different benchmarks. The experimental results demonstrate that this method outperforms other prior approaches, and its average accuracy reaches 97.328% in a four-way five-shot problem using few support samples.

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Section: Literature Reviewmentioning

confidence: 99%