Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNNLSTM Framework

Release time:2021-06-28

Impact Factor:2.412

DOI number:10.3390/electronics9111804

Journal:Electronics

Key Words:ground penetrating radar (GPR); hyperbola region detection; Convolutional Neural Network (CNN); Long Short-Term Memory (LSTM); hyperbola classification; diameter identification

Abstract:Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. owever, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

Co-author:罗佳斌

First Author:雷文太

Indexed by:Journal paper

Discipline:Engineering

First-Level Discipline:Information and Communication Engineering

Document Type:J

Translation or Not:no

Date of Publication:2020-10-31

Included Journals:SCI、EI