Passive Mine Detection and Classification Method Based on Hybrid Model

Yılmaz, Cemal; Kahraman, Hamdi Tolga; Soyler, Salih

doi:10.1109/access.2018.2866538

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…The results which were obtained with this study have been compared with the similar ones in the literature. * Yılmaz et al performed passive measurements in their studies, used the MA method, and classified five different types of mines at 85.8% [37]. In our novel study, 10 different types of explosives were classified with 95% performance.…”

Section: Discussionmentioning

confidence: 91%

“…In our study, independent measurements without being effected by position were made with fluxgate sensors. * In the previous studies [29,33,37] the measurement results changed depending on the sensor position, which made it difficult to make real-time measurements. By the measuring circuit developed in our study, position-independent measurements can be performed.…”

Section: Discussionmentioning

confidence: 99%

“…kNN classification algorithm is preferred to classify explosive and non-explosive materials. In the literature, it is clear that explosives can be detected by a kNN classification algorithm and significant results have been obtained [37,39].…”

Section: Classification Algorithmmentioning

confidence: 99%

“…In other words, different anomalies occur in the region where the object is buried compared to the region where the object is not buried. This measurement is a passive technique, and BEs and IEDs can be detected by evaluating these anomalies [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Fluxgate or anisotropic magneto resistive (AMR) sensor arrays have been integrated to three-axis motion systems in explosive and buried object detection studies with the MA method in the literature, and data have been collected. Although the sensor arrays were moved along the x, y, and z axes during the data acquisition phase, the positions of the sensors were not changed [33][34][35][36][37]. However, even if there is no MA, changing the position of the sensor creates voltage variations at the output of the sensors.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

2019

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Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 × 25 matrices, and then they were reduced to 32 × 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 × 25 and 32 × 2 data matrices, respectively. Classification times for 32 × 25 and 32 × 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Classification Algorithmmentioning

confidence: 99%