Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning

WANG Hui; OUYANG Shan; LIU Qinghua; LIAO Kefei; ZHOU Lijun

doi:10.11999/JEIT211032

Volume 44 Issue 4

Apr. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1284-1294

WANG Hui, OUYANG Shan, LIU Qinghua, LIAO Kefei, ZHOU Lijun. Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032

Citation:

WANG Hui, OUYANG Shan, LIU Qinghua, LIAO Kefei, ZHOU Lijun. Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032

Citation:

WANG Hui, OUYANG Shan, LIU Qinghua, LIAO Kefei, ZHOU Lijun. Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032

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Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning

doi: 10.11999/JEIT211032

WANG Hui^{1, 2, 3},
OUYANG Shan^{1, 3
,
,},
LIU Qinghua¹,
LIAO Kefei¹,
ZHOU Lijun⁴

1.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2.
School of Artificial Intelligence, Hezhou University, Hezhou 542899, China
3.
Satellite Navigation Positioning and Location Service National and Local Joint Engineering Research Center, Guilin University of Electronic Technology, Guilin 541004, China
4.
Shanxi Transportation Technology R&D Co., LTD, Taiyuan 030032, China

Funds: The National Natural Science Foundation of China (61871425, 61861011, 61631019), Guangxi Special Fund Project for Innovation-driven Development (GuikeAA21077008), Shanxi Transportation Department Projects (2019-1-18)

Received Date: 2021-09-27
Accepted Date: 2022-02-23
Rev Recd Date: 2022-02-18

Available Online: 2022-03-07

Publish Date: 2022-04-18

Abstract

Abstract

To solve the problem of the difficulty of target feature extraction and low recognition accuracy in Ground Penetrating Radar (GPR) two-dimensional profile, a deep learning method is used to extract the characteristic hyperbola of targets in B-SCAN image. Physics-based of GPR, a cascade Convolutional Neural Network (CNN) is designed to detect and remove the direct wave interference signal in the echo data. Then, the B-SCAN image is obtained by CNN, and the characteristic signals are classified and recognized to extract the characteristic hyperbola of the target. Meanwhile, in order to deal with the problem that the interference signals affect the structural integrity of the feature hyperbola, a feature data completion method based on directional guidance is proposed to improve the accuracy of the feature hyperbola recognition results. Compared with Histogram of Oriented Gradients(HOG) algorithm, You Only Look Once V3(YOLOV3) algorithm and Faster Region-based Convolutional Neural Network(Faster RCNN) algorithm, the detection result of the proposed method is the best in the comprehensive evaluation index F.
- Ground Penetrating Radar(GPR),
- Deep learning,
- Feature detection,
- Convolutional Neural Network(CNN)

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References(24)

References

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