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Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework
| Content Provider | MDPI |
|---|---|
| Author | Lei, Wentai Luo, Jiabin Hou, Feifei Xu, Long Wang, Ruiqing Jiang, Xinyue |
| Copyright Year | 2020 |
| Description | 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. However, 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. |
| Starting Page | 1804 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics9111804 |
| Journal | Electronics |
| Issue Number | 11 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-31 |
| Access Restriction | Open |
| Subject Keyword | Electronics Imaging Science Ground Penetrating Radar (gpr) Hyperbola Region Detection Convolutional Neural Network (cnn) Long Short-term Memory (lstm) Hyperbola Classification Diameter Identification |
| Content Type | Text |
| Resource Type | Article |
