Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization

Long, Zhiling; Wei, Wei; Turlapaty, Anish C.; Du, Qian; Younan, Nicolas H.

doi:10.1109/tns.2013.2247062

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Distinguishing between DU and DU oxide is important because DU oxides present a higher nuclear hazard; hence, their removal needs to be prioritised [29]. However, radiation detection techniques [42] are not able to distinguish between these two metals, hence the need for complementary information from the EMI data.…”

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

“…These applications employ a plethora of techniques and algorithms that cuts across the three levels of MSDF. However, of interest in this paper is the relatively recent fusion of data from radiological and contextual sensors in non-medical applications [ 8 , 24 , 25 , 26 , 27 , 28 , 29 ]. Furthermore, since the interest is on the key role played by the contextual sensors in radiation detection and imaging, applications with the fusion of multiple datasets from only radiation detectors [ 30 , 31 , 32 ] are not included in the following review.…”

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

“…Another example of the high level fusion of radiological and contextual sensor was presented in [ 29 ] where decision-level fusion was used to integrate radiation and electromagnetic induction (EMI) data in order to detect and distinguish between buried depleted uranium (DU) and its oxide. Distinguishing between DU and DU oxide is important because DU oxides present a higher nuclear hazard; hence, their removal needs to be prioritised [ 29 ]. However, radiation detection techniques [ 42 ] are not able to distinguish between these two metals, hence the need for complementary information from the EMI data.…”

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

“…However, it must be noted that these simulations assume an idealised scenario where the effects of noise, both from background radiation and clutter in the radiation and radar images, respectively, were not considered. However, techniques for dealing with noise encountered both in radiation and radar images from the field have been presented in the literature [ 29 , 56 ]. In addition, the retrieved depth of the contamination assumes that each pipe’s thickness is negligible so that the distances from the ground surface to both the pipe’s surface and the pipe’s crown (actual location of the contamination) are approximately the same.…”

Section: Combined Gpr and Radiation Imaging For 3d Localisation Ofmentioning

confidence: 99%

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Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Ukaegbu

Gamage

2017

Sensors

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Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations.

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Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

Section: Combined Gpr and Radiation Imaging For 3d Localisation Ofmentioning

confidence: 99%

See 2 more Smart Citations

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Ukaegbu

Gamage

2017

Sensors

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“…To test the performance of the detection techniques, measurements involving a group of simulation targets were performed [14]. The targets were placed in a parking lot and the area was surveyed with sodium iodide (NaI) scintillation detectors.…”

Section: A Measurement Datamentioning

confidence: 99%

Decision fusion for improved detection of buried radioactive objects

Long

Wei³

et al. 2013

2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC)

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Detection of buried radioactive objects faces challenges such as low energy counts and strong background clutters due to the burial of the targets. Classical detection methods such as the constrained energy minimization (CEM) and the RX method, when applied separately, may not be able to yield satisfactory results. In this paper, we propose to combine detection results from individual detectors through decision fusion, so that improved detection performance may be achieved. For a given data set, different detection methods are employed to yield binary decisions ("radiation" or "non-radiation") separately. Then the decisions are fused using majority voting. In addition, the scores generated from each detector (before yielding binary decisions) are also utilized to assist the fusion process. The proposed method is demonstrated to improve over the individual detectors using survey data collected on simulation targets.

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Detecting and tracking multiple mobile radioactive sources by data fusion of a surveillance camera and a sodium iodide (NaI) detector

Ardiny

Beigzadeh

Askari

2022

Review of Scientific Instruments

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Nowadays, the use of radioactive materials has increased due to recent progress in nuclear technology, but concerns about the missing or smuggling of radioactive materials have persisted. Nuclear threats such as terrorist attacks or the malicious use of radioactive materials out of regulatory control (MORC) are always severe problems that can result in adverse environmental, health, economic, and security effects. Applying new technologies to monitor and track MORC will prevent radioactive materials from being illegally transported across borders or from one place to another. In this research, we have constructed a detection system to automatically and remotely localize multiple mobile gamma-emitting radiation sources among other objects by combining an IP camera and a sodium iodide detector. An algorithm for the detection system has been developed to identify the objects’ paths from camera data and correlate radiation data with the paths to detect contaminated objects. We evaluated the system using two weak radioactive sources (Co-60, Cs-137), which were hidden on moving objects, and succeeded in finding harmful targets. The results have also shown that by increasing the number of contaminated sources from one to two, the detection accuracy decreases by about 2.5 times. However, by doubling the speed of mobile targets, the detection accuracy improved by about 30%. Detecting and tracking MORC with a single detection system is limited to small regions. By equipping many surveillance cameras in a city with relatively inexpensive radioactive detectors, a network sensing system is established to find radioactive hotspots in a smart city.

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Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization

Cited by 9 publications

References 14 publications

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Decision fusion for improved detection of buried radioactive objects

Detecting and tracking multiple mobile radioactive sources by data fusion of a surveillance camera and a sodium iodide (NaI) detector

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