GPR Image Classification of Buried Objects using Deep Learning with Attention Mechanism

GPR Image Classification of Buried Objects using Deep Learning with Attention Mechanism
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-4
Year of Publication : 2025
Author : T. Kalaichelvi, S. Ravi, Buddepu Santhosh Kumar
DOI : 10.14445/22315381/IJETT-V73I4P103

How to Cite?
T. Kalaichelvi, S. Ravi, Buddepu Santhosh Kumar, "GPR Image Classification of Buried Objects using Deep Learning with Attention Mechanism," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp. 24-33, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P103

Abstract
The contamination of landmines and other unexploded ordnances has become a global safety problem. There is an urgent need to identify and demine the buried unexploded ordnance from the subsurface to save people and civilians from severe injury and death. Ground Penetrating Radar (GPR) is the best geophysical imaging technique for identifying and recognizing underground objects. It retrieves large GPR B-Scan datasets from the subsurface to be processed and classified using deep learning techniques. The proposed technique describes GPR B-Scan images of buried objects like landmines with casing materials made of metal and plastic, which can be classified using deep learning with attention mechanisms with MobileNetV2 as the base model. It uses deep learning architecture to successfully extract essential features and recognize them from GPR images of metal pipes, metal tiffin boxes, and plastic tiffin boxes. It includes creating novel datasets on gprMax software with the real-time scenario of a homogeneous medium, added roughness, water, and grass to support a heterogeneous soil medium with different relative permittivity, casing material, object size, and burial depth. The process initiates with the preprocessing of GPR data, applying data augmentation, splitting the datasets into training and testing, and building a deep learning architecture with attention mechanism; finally, training the network model and testing for GPR image classification. The multi-class classification evaluated and showcased improved performance using deep learning with attention mechanism than traditional methods. The developed model can potentially support new inventions in archaeology, infrastructure assessment, and explosive ordnance identification.

Keywords
Attention mechanism, Buried object, Data augmentation, Deep learning, Ground penetrating radar, Image classification, Landmine, MobileNetV2.

References
[1] Foreign Anti-Tank Landmine Kit-Inert Replicas, Inert Products LLC, 2023. [Online]. Available: https://inertproducts.com/product/inert-anti-tank-landmine-kit/
[2] Protecting Lives, ICBL/CMC, 2025. [Online]. Available: https://www.icblcmc.org/
[3] Anti-Personnel Landmines Convention, United Nations, 2019. [Online]. Available: https://disarmament.unoda.org/anti-personnel-landmines-convention/
[4] Landmine, Explosive Remnants of War (ERW), and Cluster Submunition Casualties in 2019, ReliefWeb, 2020. [Online]. Available: https://reliefweb.int/map/world/landmine-explosive-remnants-war-erw-and-cluster-submunition-casualties-2019
[5] Saman Ghaffarian et al., “Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review,” Remote Sensing, vol. 13, no. 15, pp. 1-22, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Rudong Xu et al., “Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification,” Remote Sensing, vol. 10, no. 10, pp. 1-29, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiang Li et al., “Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds,” International Journal of Network Dynamics and Intelligence, vol. 2, no. 1, pp. 93-116, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Haoyuan Chen et al., “IL-MCAM: An Interactive Learning and Multi-Channel Attention Mechanism-Based Weakly Supervised Colorectal Histopathology Image Classification Approach,” Computers in Biology and Medicine, vol. 143, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Fabio Giovanneschi et al., “Dictionary Learning for Adaptive GPR Landmine Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 10036-10055, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Zeliang Zong et al., “A Deep Learning Approach for Urban Underground Objects Detection from Vehicle-Borne Ground Penetrating Radar Data in Real-Time,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 293-299, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Hai Liu et al., “Automatic Recognition and Localization of Underground Pipelines in GPR B-Scans Using a Deep Learning Model,” Tunnelling and Underground Space Technology, vol. 134, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] N. Smitha, and Vipula Singh, “Target Detection Using Supervised Machine Learning Algorithms for GPR Data,” Sensing and Imaging, vol. 21, no. 1, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wentai Lei et al., “Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans Via the CNN-LSTM Framework,” Electronics, vol. 9, no. 11, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zhimin Gong, and Huaiqing Zhang, “Research on GPR Image Recognition Based on Deep Learning,” International Conference on Computer Science Communication and Network Security, vol. 20, pp. 447-454, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Namgyu Kim et al., “A Novel 3D GPR Image Arrangement for Deep Learning-Based Underground Object Classification,” International Journal of Pavement Engineering, vol. 22, no. 6, pp. 740-751, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hui Wang et al., “Buried Target Detection Method for Ground Penetrating Radar Based on Deep Learning,” Journal of Applied Remote Sensing, vol. 16, no. 1, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Maxwell M. Omwenga et al., “Cognitive GPR for Subsurface Object Detection Based on Deep Reinforcement Learning,” IEEE Internet Things Journal, vol. 8, no. 14, pp. 11594-11606, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Safdar Mahmood et al., “Detecting Improvised Landmines Using Deep Neural Networks on GPR Image Dataset Targeting FPGAs,” 2022 IEEE Nordic Circuits and Systems Conference, Oslo, Norway, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Rakeeb Mohamed Jaufer et al., “A Preliminary Numerical Study to Compare the Physical Method and Machine Learning Methods Applied to GPR Data for Underground Utility Network Characterization,” Remote Sensing, vol. 14, no. 4, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xu Bai et al., “3D-GPR-RM: A Method for Underground Pipeline Recognition Using 3-Dimensional GPR Images,” Applied Sciences, vol. 13, no. 13, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Tsukasa Mizutani, Jingzi Chen, and Shuto Yotsumoto, “The 3D Localization of Subsurface Pipes from Ground Penetrating Radar Images Using Edge Detection and Point Cloud Segmentation,” Measurement, vol. 236, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mehmet Sezgin, and Mahmut Nedim Alpdemir, “Classification of Buried Objects Using Deep Learning on GPR Data,” 2023 IEEE International Conference on Advanced Systems and Emergent Technologies, Hammamet, Tunisia, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Yang Su et al., “End-To-End Deep Learning Model for Underground Utilities Localization Using GPR,” Automation in Construction, vol. 149, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Xiaoxia Meng et al., “Few-Shot Image Classification Algorithm Based on Attention Mechanism and Weight Fusion,” Journal of Engineering and Applied Science, vol. 70, no. 1, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Zahid Ullah et al., “Densely Attention Mechanism Based Network for COVID-19 Detection in Chest X-Rays,” Scientific Reports, vol. 13, no. 1, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Yanmei Zheng, Guanghui Zhou, and Bibo Lu, “A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism,” Applied Sciences, vol. 13, no. 14, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] XiaLin Zhang et al., “Rock Thin Section Image Classification Based on Depth Residuals Shrinkage Network and Attention Mechanism,” Earth Science Informatics, vol. 16, no. 2, pp. 1449-1457, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Zhihao Huang et al., “Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism,” Applied Sciences, vol. 13, no. 5, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Heng Zhang et al., “Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network,” Remote Sensing, vol. 16, no. 12, pp. 1-29, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Ge Junkai et al., “GPR-TransUNet: An Improved TransUNet Based on Self-Attention Mechanism for Ground Penetrating Radar Inversion,” Journal of Applied Geophysics, vol. 222, 2024.
[CrossRef] [Google Scholar] [Publisher Link]