The Detection of Buried Pipes From Time-of-Flight Radar Data

Borgioli, G.; Capineri, Lorenzo; Falorni, Pierluigi; Matucci, Serena; Windsor, C. G.

doi:10.1109/tgrs.2008.917211

Cited by 76 publications

(35 citation statements)

References 15 publications

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“…The randomized Hough transform (RHT), as one of the popular variants of the Hough transform [24][25][26], applies random sampling and converging mapping strategy to overcome the drawbacks of Hough transform regarding computational cost, detection accuracy, and resistance to noise [25,26]. As a result, the RHT has been applied widely in actual automatic recognition of landmines and pipelines in GPR images [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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Abstract:As a nondestructive geophysical tool, Ground penetrating radar (GPR) has been applied in tree root study in recent years. With increasing amounts of GPR data collected for roots, it is imperative to develop an efficient automatic recognition of roots in GPR images. However, few works have been completed on this topic because of the complexity in root recognition problem. Based on GPR datasets from both controlled and in situ experiments, the randomized Hough transform (RHT) algorithm was evaluated in root object recognition for different center frequencies (400 MHz, 900 MHz, and 2000 MHz) in this paper. Reasonable accuracy was obtained (both a high recognition rate and a low false alarm rate) in these datasets, which shows it is feasible to apply the RHT algorithm for root recognition. Furthermore, we evaluated the influence of root and soil factors on the recognition. We found that the performance of RHT algorithm is mainly affected by root interval length, root orientation, and clutter noise of soil. The recognition results by RHT could be applied for large scale root system distribution study in belowground ecology. Further studies should be conducted to reduce clutter noise and improve the recognition of the complex root reflections.

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Section: Introductionmentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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“…To this purpose, both model-based detection methods and featuresbased techniques have been proposed. Typical model-based approaches aim to individuate hyperbola in GPR images by making use of Hough transform [9] or fitting techniques [10]. However, the sensitivity of GPR systems to changes in local environmental conditions results in highly variable responses from buried objects that hinders correct hyperbola detection.…”

Section: Introductionmentioning

confidence: 99%

Landmine detection from GPR data using convolutional neural networks

Lameri

Lombardi

Bestagini

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

105

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Abstract-The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to groundpenetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles.

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“…BOD has been extensively used in civil engineering [1,2] and military applications [3], such as landmine or buried water pipe detection, etc.…”

Section: Introductionmentioning

confidence: 99%

Iterative Time-Reversal Mirror Method for Imaging the Buried Object Beneath Rough Ground Surface

Zhu¹,

Zhao²,

Yang³

et al. 2011

PIER

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Abstract-An iterative Time-Reversal Mirror (TRM) method is proposed to Detect and Image the buried target beneath ground surface. Unlike the conventional TRM methods which treat the information of the ground as clutters and directly delete them, the iterative TRM imaging method proposed in this paper utilizes the information of rough ground surface as a useful knowledge. The new approach is consisted of two TRM procedures. In the first TRM procedure, it aims to image the rough surface where the propagation environment for electromagnetic wave is free space. The second TRM procedure aims to image the buried target. In this step, the information of the rough surface estimated by the first TRM procedure will be treated as newly updated propagation environment. Then conventional TRM is applied to image the buried target. By applying this iterative TRM method, the information of the rough ground can be well considered in the whole TRM procedure. Numerical simulations prove that this method performs significantly better image contrast comparing with the results obtained by using conventional TRM. 4-5 dB improvement on the imaging SNR has been achieved. Furthermore, the target can be located more accurately.

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The Detection of Buried Pipes From Time-of-Flight Radar Data

Cited by 76 publications

References 15 publications

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Landmine detection from GPR data using convolutional neural networks

Iterative Time-Reversal Mirror Method for Imaging the Buried Object Beneath Rough Ground Surface

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