Anti-personnel Mine Detection and Classification Using GPR Image

Bhuiyan, Mohammad Arif Sobhan; Nath, Baikunth

doi:10.1109/icpr.2006.274

Cited by 14 publications

(16 citation statements)

References 6 publications

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“…They consist in summing up intensities along A-scans within a C-scan, and thus, a surface 2-D map of potentially suspicious places is generated [1]. In [5], Bhuiyan and Nath worked with B-scans and use a seeded-growing approach for image segmentation and discovering places of interest. Marble [2] also considered B-scans and proposed a binary large objects detection technique.…”

Section: A Review Of Some Detection Approachesmentioning

confidence: 99%

“…Overlapping regions of 3 × 3 cells constitute blocks for the normalization (33). 5 Since the extraction is repeated for B-scans (crossing the middle of the scanning window) both across and along track, the total number of features is twice the grid size times the number of bins: n = 2 · 4 · 3 · n θ . In other words, the full vector of HOG features is a concatenation of H(c, l) values for all cells, all bins, and two B-scan orientations.…”

Section: A Hog Featuresmentioning

confidence: 99%

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Fast Analysis of C-Scans From Ground Penetrating Radar via 3-D Haar-Like Features With Application to Landmine Detection

Klęsk

Godziuk

Kapruziak

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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This paper aimed to devise an efficient algorithm applicable to ground penetrating radar (GPR) and to enable an automatic landmine detection. Proposed is a machine learning approach in which we put the main emphasis on fast performance of the scanning procedure analyzing the C-scans, i.e., 3-D images defined over the coordinate system, i.e., along track by across track by time, where the time axis can be associated with depth. The approach is based on our proposition of 3-D Haar-like features. Learning of the detector is carried out by boosted decision trees.Practical experiments on metal and plastic antitank mines in a garden soil are carried out. A prototype mobile platform is designed to scan the subsurface of the ground, equipped with a GPR based on a standard vector network analyzer and our original antenna system. We report the results, particularly the following: detection sensitivity, false alarm rates, receiver operating characteristic curves, and times of learning and detection.

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Section: A Review Of Some Detection Approachesmentioning

confidence: 99%

Section: A Hog Featuresmentioning

confidence: 99%

Fast Analysis of C-Scans From Ground Penetrating Radar via 3-D Haar-Like Features With Application to Landmine Detection

Klęsk

Godziuk

Kapruziak

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…Gamba and Lossani [5] also used the similar method but the Neural Network aims at the target signatures that were enhanced and processed beforehand. Bhuiyan and Nath [6] differentiate target reflections using region growing approach which is constrained by the image quality. In [7], Pasolli et.al detects buried targets by localizing hyperbolic patterns using Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…For that reason, automatic target detection in GPR images can give a significant leap in the practical application of GPR. Neural Network classifier [1][5] [6] has been one of the most commonly used approach to recognize the hyperbolic signatures Al-Nuaimy et.al [1] trained a backpropagation neural network to recognize the spectral contents of signal segments of targets. Gamba and Lossani [5] also used the similar method but the Neural Network aims at the target signatures that were enhanced and processed beforehand.…”

Section: Introductionmentioning

confidence: 99%

Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)

Lee

Mokji

2014

2014 2nd International Conference on Electronic Design (ICED)

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Ground Penetrating Radar (GPR) has proven itself to be one of the most popular and reliable geophysical device in subsurface investigation. However, human operators are required to manually interpret the GPR data. In a typical geophysical survey, collected GPR data sometimes can be enormously huge, causing issues such as time consuming and inaccuracy in results due to human errors. In this paper, we present an algorithm that automatically detects hyperbolic signatures in GPR data in B-scan model. This developed algorithm is able to mark potential regions that contain the reflections from target of buried objects. Histogram of Oriented Gradients (HOG) was initially developed to detect pedestrians, but it can be also well-adapted to detect particular shapes and objects. HOG descriptors are extracted from a set of training images and are trained using a linear SVM classifier. The main purpose of this algorithm is to narrow down the data into possible target reflection regions. After that, we implement Hough Transform to highlight the hyperbolic patterns in the reflection. The results shows that the developed system can perform target detection at an average of 93.75% detection rate for all four test sets.

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“…In this context, the objectives can be the obtaining of a detection warning signal (DWS) along the scanning path, 2D depth imaging of the scanning line or 3D imaging of the suspicious region in both depth and moving direction. Identification processes [2][3][4][5][6][7] can be applied after the buried object location is determined. There are numerous methods to detect buried objects utilizing GPR; linear prediction [8][9][10], principal component analysis [11,12], independent component analysis [11], wavelet domain [13], frequency domain correlation [14,15], time domain correlation [16], linear minimum mean square error estimation, [17], Gumbel distribution [18], and least square-based [19] methods can be given in this scope.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Sezgin

2011

EURASIP J. Adv. Signal Process.

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In this article, a simultaneous buried object detection and imaging method is proposed for time domain ground penetrating radar (GPR) data. Fuzzy weighted background removal is applied to the data through a sliding window and then target energy functions are obtained by means of convolution summations of consecutive A-scan signals in an appropriate manner. An auxiliary detection function is proposed as an emphasized detection test statistic and then an automatic detection warning signal creation method is devised. The proposed method has been tested over a set of small-sized surrogate anti-personnel (AP) mines which are not easily detectable and medium-sized surrogate AP and Anti-tank mines. The results are promising as nearly full detection performance. Zero false alarm rate is achieved in this dataset without remarkable corruption in estimated target GPR images. Moreover, it is observed that the noise immunity of the proposed method is highly satisfactory in terms of detection probability.

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Anti-personnel Mine Detection and Classification Using GPR Image

Cited by 14 publications

References 6 publications

Fast Analysis of C-Scans From Ground Penetrating Radar via 3-D Haar-Like Features With Application to Landmine Detection

Fast Analysis of C-Scans From Ground Penetrating Radar via 3-D Haar-Like Features With Application to Landmine Detection

Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

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