Next Article in Journal
Robust DOA Estimation via a Deep Learning Framework with Joint Spatial–Temporal Information Fusion
Previous Article in Journal
Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 25
Issue 10
10.3390/s25103138
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition

by
Li Liu
1,
Dajiang Yu
1,
Xiping Zhang
1,
Hang Xu
1,
Jingxia Li
1,*,
Lijun Zhou
2 and
Bingjie Wang
1
1
Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education, College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China
2
Shanxi Intelligent Transportation Institute Co., Ltd., Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(10), 3138; https://doi.org/10.3390/s25103138
Submission received: 19 March 2025 / Revised: 30 April 2025 / Accepted: 13 May 2025 / Published: 15 May 2025
(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)
Versions Notes

Abstract

Ground penetrating radar (GPR) is an effective and efficient nondestructive testing technology for subsurface investigations. Deep learning-based methods have been successfully used in automatic underground target recognition. However, these methods are mostly based on supervised learning, requiring large amounts of labeled training data to guarantee high accuracy and generalization ability, which is a challenge in GPR fields due to time-consuming and labor-intensive data annotation work. To alleviate the demand for abundant labeled data, a semi-supervised deep learning method named attention–temporal ensembling (Attention-TE) is proposed for underground target recognition using GPR B-scan images. This method integrates a semi-supervised temporal ensembling architecture with a triplet attention module to enhance the classification performance. Experimental results of laboratory and field data demonstrate that the proposed method can automatically recognize underground targets with an average accuracy of above 90% using less than 30% of labeled data in the training dataset. Ablation experimental results verify the efficiency of the triplet attention module. Moreover, comparative experimental results validate that the proposed Attention-TE algorithm outperforms the supervised method based on transfer learning and four semi-supervised state-of-the-art methods.
Keywords: ground penetrating radar; underground target recognition; deep learning; semi-supervised learning; temporal ensembling; triplet attention ground penetrating radar; underground target recognition; deep learning; semi-supervised learning; temporal ensembling; triplet attention

Share and Cite

MDPI and ACS Style

Liu, L.; Yu, D.; Zhang, X.; Xu, H.; Li, J.; Zhou, L.; Wang, B. A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition. Sensors 2025, 25, 3138. https://doi.org/10.3390/s25103138

AMA Style

Liu L, Yu D, Zhang X, Xu H, Li J, Zhou L, Wang B. A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition. Sensors. 2025; 25(10):3138. https://doi.org/10.3390/s25103138

Chicago/Turabian Style

Liu, Li, Dajiang Yu, Xiping Zhang, Hang Xu, Jingxia Li, Lijun Zhou, and Bingjie Wang. 2025. "A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition" Sensors 25, no. 10: 3138. https://doi.org/10.3390/s25103138

APA Style

Liu, L., Yu, D., Zhang, X., Xu, H., Li, J., Zhou, L., & Wang, B. (2025). A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition. Sensors, 25(10), 3138. https://doi.org/10.3390/s25103138

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop