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

Feifei Hou, Xu Liu, Xinyu Fan and Ying Guo ()
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Feifei Hou: School of Automation, Central South University, Changsha 410083, China
Xu Liu: School of Automation, Central South University, Changsha 410083, China
Xinyu Fan: School of Automation, Central South University, Changsha 410083, China
Ying Guo: School of Automation, Central South University, Changsha 410083, China

Mathematics, 2022, vol. 10, issue 15, 1-18

Abstract: 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.

Keywords: ground-penetrating radar (GPR); cavity morphology recognition; few-shot learning (FSL); deep learning (DL); relation network (RelationNet) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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