Area-based results for mine detection

Gelenbe, Erol; Koçak, Taşkın

doi:10.1117/12.324259

Cited by 21 publications

(21 citation statements)

References 6 publications

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“…One technique that we use for pre-processing the minefield data is the δ-technique [6,7]. This technique significantly reduces false alarm rates by making use of neighborhood or area information around each location.…”

Section: δ-Techniquementioning

confidence: 99%

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

Koçak

Draper

2006

Neural Process Lett

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Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a BackPropagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the δ-technique. Our results show that the combination of the δ-technique and the S-statistics with a neural network improves the performance.

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Section: δ-Techniquementioning

confidence: 99%

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

Koçak

Draper

2006

Neural Process Lett

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“…The G-networks systems have a rich and wide range of real applications in areas such as computers, communications and manufacturing (see [13,14,16,[19][20][21][22][23][24][25]). For a recent systematic account of the main methods and results on this topic, we refer to Gelenbe [17,18] , Artalejo [2] and references therein.…”

Section: Introductionmentioning

confidence: 99%

A single-server discrete-time retrial G-queue with server breakdowns and repairs

Wang

Zhang

2009

Acta Math. Appl. Sin. Engl. Ser.

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This paper concerns a discrete-time Geo/Geo/1 retrial queue with both positive and negative customers where the server is subject to breakdowns and repairs due to negative arrivals. The arrival of a negative customer causes one positive customer to be killed if any is present, and simultaneously breaks the server down. The server is sent to repair immediately and after repair it is as good as new. The negative customer also causes the server breakdown if the server is found idle, but has no effect on the system if the server is under repair. We analyze the Markov chain underlying the queueing system and obtain its ergodicity condition. The generating function of the number of customers in the orbit and in the system are also obtained, along with the marginal distributions of the orbit size when the server is idle, busy or down. Finally, we present some numerical examples to illustrate the influence of the parameters on several performance characteristics of the system.

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“…It has been shown that the time-domain response can be modeled using a weighted sum of decaying exponentials. [2][3][4][5][6][7][8] In general, the decay rates of metallic objects (e.g., mines) are greater than that of the earth. The sensor used in this study, the F3 metal detector built by the MineLab Corporation, processes this received signal further in order to provide ground cancellation.…”

Section: Introductionmentioning

confidence: 99%

“…5,6,11 Due to real-time operating constraints, the complexity of the algorithm must be minimized. In addition, the algorithm must be robust to the movement of the sensor as it is not desirable to have an algorithm that depends on the speed of movement or distance between the sensor head and the buried object.…”

Section: Introductionmentioning

confidence: 99%

Man versus machine: robust regional processing of EMI data

Huettel

Baier

Collins

2004

Detection and Remediation Technologies for Mines and Minelike Targets IX

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The handheld F3 metal detector, developed by the MineLab Corporation, measures the responses of buried objects to electromagnetic pulses. These responses can be processed to determine whether a landmine is present. The simplest processor calculates the total energy in the response, thereby reducing the entire spatial and temporal response to a single value. This value, proportional to the amount of metal in the object, can then be compared to a predetermined threshold. The drawback of this common approach is that, although the threshold may be set so that few, if any, mines are missed, doing so may result in a high false alarm rate. Previous work has demonstrated that incorporating physics-based features into a Bayesian detection framework and performing simple, one-dimensional regional processing can significantly reduce the false alarm rate while maintaining the desired level of detection. 1 Based on these promising results, this approach has been extended to incorporate twodimensional regional processing. At the test site, data was collected both manually and robotically using nearly identical protocols. Thus, in theory, measured responses should be similar and algorithm performance equivalent whether the detector was operated by a robot or a human. The robustness of various algorithms was evaluated by comparing performance across manual and robotic data sets. Certain physics-based feature detectors were relatively unaffected by the response variability introduced unintentionally by the human operator. However, other algorithms that incorporate more sensitive, often regional, features were able to provide greater gains for the robotic data set than for the manual data set. These results imply that there may be a tradeoff between performance and practical issues that need to be addressed when selecting an algorithm for implementation in a field setting.

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Area-based results for mine detection

Cited by 21 publications

References 6 publications

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

A single-server discrete-time retrial G-queue with server breakdowns and repairs

Man versus machine: robust regional processing of EMI data

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