Publications
Journal Articles
2023
1.
Nairit Barkataki; Banty Tiru; Utpal Sarma
Size estimation of underground targets from GPR frequency spectra: A deep learning approach Journal Article
In: Journal of Applied Geophysics, vol. 213, pp. 105009, 2023, ISSN: 0926-9851.
Abstract | BibTeX | Tags: Archeology, Artificial Neural Network, Bridge inspection, Civil Engineering, Deep learning, Ground penetrating radar, neural networks, object size prediction | Links:
@article{barkataki2023size,
title = {Size estimation of underground targets from GPR frequency spectra: A deep learning approach},
author = {Nairit Barkataki and Banty Tiru and Utpal Sarma},
url = {https://nairit.in/wp-content/uploads/Paper009_JAG_ObjectSizeAScan.pdf},
doi = {10.1016/j.jappgeo.2023.105009},
issn = {0926-9851},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Journal of Applied Geophysics},
volume = {213},
pages = {105009},
abstract = {GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies have increasingly focused on analysis in the frequency domain. This paper proposes an artificial neural network (ANN) model for estimating the diameter of underground rods (solid) and pipes (hollow). GPR data captured in the time domain domain is transformed to the frequency domain using FFT, after which feature extraction is performed using ANN. An FPGA-based prototype GPR system is used to collect GPR A-Scan data for a variety of targets made of aluminium, stainless steel, rebar, and PVC. A mean absolute percentage error of 1.89% is achieved using the proposed model. The experimental results confirm the effectiveness of the proposed approach in extracting size-related information from GPR data.},
keywords = {Archeology, Artificial Neural Network, Bridge inspection, Civil Engineering, Deep learning, Ground penetrating radar, neural networks, object size prediction},
pubstate = {published},
tppubtype = {article}
}
GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies have increasingly focused on analysis in the frequency domain. This paper proposes an artificial neural network (ANN) model for estimating the diameter of underground rods (solid) and pipes (hollow). GPR data captured in the time domain domain is transformed to the frequency domain using FFT, after which feature extraction is performed using ANN. An FPGA-based prototype GPR system is used to collect GPR A-Scan data for a variety of targets made of aluminium, stainless steel, rebar, and PVC. A mean absolute percentage error of 1.89% is achieved using the proposed model. The experimental results confirm the effectiveness of the proposed approach in extracting size-related information from GPR data.
2022
2.
Nairit Barkataki; Banty Tiru; Utpal Sarma
A CNN model for predicting size of buried objects from GPR B-Scans Journal Article
In: Journal of Applied Geophysics, vol. 200, pp. 104620, 2022, ISSN: 0926-9851.
BibTeX | Tags: CNN, Deep learning, Ground penetrating radar, object size prediction | Links:
@article{barkataki2022cnn,
title = {A CNN model for predicting size of buried objects from GPR B-Scans},
author = {Nairit Barkataki and Banty Tiru and Utpal Sarma},
url = {https://nairit.in/wp-content/uploads/2023/01/Paper003_JAG_ObjectSizeDL.pdf},
doi = {10.1016/j.jappgeo.2022.104620},
issn = {0926-9851},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Applied Geophysics},
volume = {200},
pages = {104620},
publisher = {Elsevier},
keywords = {CNN, Deep learning, Ground penetrating radar, object size prediction},
pubstate = {published},
tppubtype = {article}
}
Conference Proceedings
2020
1.
Nairit Barkataki; Sharmistha Mazumdar; Rajdeep Talukdar; Priyanka Chakraborty; Banty Tiru; Utpal Sarma
Prediction of Size of Buried Objects using Ground Penetrating Radar and Machine Learning Techniques Proceedings Article
In: 2020 International Conference on Computational Performance Evaluation (ComPE), pp. 781-785, IEEE 2020.
Abstract | BibTeX | Tags: classification, Ground penetrating radar, machine learning, object size prediction | Links:
@inproceedings{barkataki2020Prediction,
title = {Prediction of Size of Buried Objects using Ground Penetrating Radar and Machine Learning Techniques},
author = {Nairit Barkataki and Sharmistha Mazumdar and Rajdeep Talukdar and Priyanka Chakraborty and Banty Tiru and Utpal Sarma},
url = {https://nairit.in/wp-content/uploads/2022/12/PID6437207_Prediction-of-Nairit.pdf},
doi = {10.1109/ComPE49325.2020.9200094},
year = {2020},
date = {2020-07-01},
urldate = {2020-07-01},
booktitle = {2020 International Conference on Computational Performance Evaluation (ComPE)},
pages = {781-785},
organization = {IEEE},
abstract = {Ground penetrating radar (GPR) uses electromagnetic (EM) wave to detect the subsurface objects. Interpretation and analysis of GPR signals are still challenging tasks as it requires skilled user (geologists in most cases). Particularly difficult is the prediction of the object sizes. This paper proposes a new method for predicting size of buried objects. First, standard scaling pre-processing techniques are used to optimise the B-Scan data. The features are then supplied to Random Forest (RF) and Support Vector Machine (SVM) classifiers to automatically predict the size of the buried object. The proposed feature based RF classifier shows similar performance in the accuracy of classification compared to SVM (Radial Basis Function kernel) system.},
keywords = {classification, Ground penetrating radar, machine learning, object size prediction},
pubstate = {published},
tppubtype = {inproceedings}
}
Ground penetrating radar (GPR) uses electromagnetic (EM) wave to detect the subsurface objects. Interpretation and analysis of GPR signals are still challenging tasks as it requires skilled user (geologists in most cases). Particularly difficult is the prediction of the object sizes. This paper proposes a new method for predicting size of buried objects. First, standard scaling pre-processing techniques are used to optimise the B-Scan data. The features are then supplied to Random Forest (RF) and Support Vector Machine (SVM) classifiers to automatically predict the size of the buried object. The proposed feature based RF classifier shows similar performance in the accuracy of classification compared to SVM (Radial Basis Function kernel) system.