Handheld metal detector with online visualisation and classification for the humanitarian mine clearance Hendrik Krüger and Hartmut Ewald University of Rostock Institute of General Electrical Engineering D-18059 Rostock, Germany
[email protected] Abstract—Today for clearing areas from landmines, beside dogs and searching needles, almost only metal detectors are used. Metal detectors for mine clearance are high sensitive inductive sensors which react also on small metal parts. Nevertheless, they give no further information about the buried object (depth, size, shape, material). Besides, the high false alarm rate (up to 1000 per mine) makes the mine clearance a protracted and cost-intensive process. Reasons for the false alarms are “uncooperative” soils, harmless metallic objects and the low metal content in newer anti-personnel mines.
II. SIGNALPROCESSING FOR INDUCTIVE MULTIFREQUENCEY METAL DETECTORS
A. Soil compensation There is a dramatically decrease of detection probability in soils with magnetic properties (µr>1, dispersive media) or inhomogeneous conductivity because of the dominant soil signal, especially in terms of mines with low metal-content. If the detector provides n linear independent parameter, it is well known from eddy current testing, that up to n-1 parameter can be used to suppress n-1 disturbance values. The ground compensation algorithms from the available metal detectors minimize the soil-signal without a look to the different target object signatures. But if the types of mines in the field are known, this a-priori information can be used to optimize the soil compensation. Therefore a database is used that provides signatures from mines in air. Those signatures are scanned position referenced under lab terms in various depths and orientations. Under usage of this database, the soil effect is cancelled regarding to the Mine-to-Soil-SignalRatio (MSR) defined by:
In this paper a method is described which provides more information about the buried object by using image processing and signature classification, which works also in the presence of “uncooperative” soil. Based on an algorithm for soil compensation and object classification, this paper describes a handheld system with online visualization and classification. Therefore a commercial metal detector was enhanced with additional signal processing and an ultrasonic position reference system.
I. INTRODUCTION Metal detectors based on the eddy current principle by using impulse-, single- or multi- frequency excitation convert the change of the complex received sensor signal into an acoustic signal, which signals the user or deminer the presence of a metallic object [1], [2], [3]. Determining the two-dimensional position (so called pinpointing) of a buried metal object is easily possible for a qualified deminer, however, a sure classification of the object properties (depth, size, shape and material) is almost impossible.
MSRi = ( ci ⋅ p mine ) ( ci ⋅ p soil )
The local soil is scanned on multiple points, moving the detector over a metal free area. In the first step, the signals from the soil scan (each point with n raw-data components p={p1...pn}) are analyzed by the well known Principal Component Analysis method (PCA) [5], which gives the main component vectors c1...cn representing the influence of soil. Thereby c1 represents the most soil influence and the cn the less. In most cases already the second principle component is nearly free from soil influences. (These “soil-free” components are used in the second step for optimisation of the Mine-to-Soil-SignalRatio (MSR).)
Some classification features are provided by some detectors for treasure hunting [4]. But the methods used , are not reliable in the case of small composite metal parts of different material and complex shape, mines usually consist of. Also the influence of uncooperative soil and the presence of multiple metal objects are almost neglected by object classification methods. Under practical condition this problems can be solved with image processing of the localdistributed sensor data and processing of multi parameter sensor data and usage of a-priori information for the pattern recognition.
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Therefore we distinguish between two goals. First is the detection and 2D-visulaiszation of an object.
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By comparing the (desired) soil reduction with the (unwanted) reduction of the mine signal, the subset of component vectors ci is used, that leads to the best MSR. The second goal is the classification of an unknown object. Here it is most important to maintain the information provided from the raw-data components of the multifrequency detector (Fig. 1). If the mine signal is ‘strong’ enough (high metal content or shallow buried) it is not important to get the maximum MSR. So we use all vectors ci which MSR is over a defined safety-threshold (which are n-1 in most cases). The soil compensated component c’i is the dot product of the compensation vector ci and the parameter vector p with p={UReal-f1, UImag.-f1, UReal-f2, UImag.-f2} by use of a twofrequency detector. Calculating the MSR for every mine in all depths in the database gives also a good approximation of the maximum detection depth (MSR > 0dB) of every mine to detect in the present soil with the actual soil compensation settings.
Figure 2. Phase-plot of a mine signature in laterite soil in 2 and 10 cm depth, the detector is moved over the centre of the mine M2B: before (a, b) and after soil compensation (c, d).
Because of this fact, it is necessary, that the signatures from the mine database and the field scan (unknown signature) passes the same signal processing algorithms before feature extraction and classification (see Fig. 3). The evaluation of this algorithm on the test lane CTROBenkovac (Croatia, October 2006) shows, that the mines can be separated from most clutter objects with a two-frequency detector under usage of only two features (phases from the soil compensated components c’1… c’3). Further features like amplitude and signature wide can be used to specify also the depth of the mine. From 49 measurements in 3 different soils (cooperative, uncooperative, uncooperative inhomogeneous) we got only 3 false alarms that where caused from a clutter object with a signature similar to one of the 4 different mines from the database [6].
Figure 1. Signatures of clutter objects and mines scanned in air with a twofrequency metal detector.
B. Feature extraction and classification of soil compensated data As shown in Fig. 2, the signature of a mine is also considerably influenced by the soil compensation algorithm. That leads to the problem, that the in air signature of a mine stored in the database (e.g. the mine type M2B shown in Fig. 1c) and the same mine in the soil (see Fig. 2c, d) are not comparable. Figure 3. Signal processing with optimised soil compensation for visualisation and classification.
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III. HANDHELD DETECTOR All tests before where performed under use of a threeaxes-scanning-system and offline processing of the acquired data [6][7]. But the methods are also applicable to a hand held detector in real time. Therefore the standard continuous wave two-frequency metal detector Foerster Minex 2FD4.500 was modified. The detector provides 4 raw data outputs (real and imaginary part of the complex coil voltage on 2.4 and 19.2 kHz) that are connected to a Mini-Laptop for data acquisition and processing. The Laptop is also connected to an ultra sonic position reference system. A PDA is used as graphical user interface (Fig. 4).
Figure 5. Experimental setup of the position reference system for a hand held metal detector.
This accuracy is reachable using an ultrasonic reference system, like shown on Fig. 5. The ultrasonic transducer, mounted on the sensor head, sending pulses to three (four installed) extern receivers. The infrared LEDs are used to synchronise the timers. The position is determined in three dimensions by triangulation from the pulse time of flight. The knowledge of the third dimension is important, because the amplitude of the electromagnetic response caused from an object under the coil is strong influenced by the sensor height. In practice, it is difficult to align the external receiver device parallel to the ground because the terrain is not plane in most cases. Already a slightly tangential deviation leads to a misinterpreted sensor height up to some centimetres. But under the assumption, that a well trained deminer moves the sensor in a constant height, averaged over the acquisition time, displacements of the external device can be corrected afterwards by a 3D-coordinate-transformation, which minimizes the average z-variation.
Figure 4. Modification of a standard metal detector (Foerster Minex) with a PDA as user interface and an ultrasonic transducer for the position referencing.
A. The ultrasonic position reference system A lot of approaches where tested in the past to acquire position referenced data with a hand held detector. Differential GPS is often used by detection systems searching for unexploded ordinances (UXO) but it can not provide the resolution needed for landmine detection. Solutions based on acceleration sensors are only applicable on a short path because of the drift sensitivity caused by the two integrations from acceleration over speed to path. There are also solutions with optical sensors that are based on PIV [8], correlation or spatial filtering techniques [9]. Because the detector mounted CCD sensor track the soil surface structure no external device is needed. But in practice the high dynamic range of light and shadow, grass or moving shadows from trees avoid accuracy in the range of millimetres.
Further artefacts, typical for a manual moved metal detector, are to fast movements and outliers from the average sensor height. Such faulty measurement points are easy to identify and filtered from the dataset. The resolution achieved with the described setup is better than 2 mm and the accuracy over the whole sensing area of about one square meter is below 5mm. B. Imaging and classification of irregular spaced sensor data Imaging the senor data is done by gridding the irregular sampled position referenced dataset to an equidistant mash. This can be done by the Inverse Distance Weighting method [10]. The value of a not sampled position Xi is determined by
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a weighted average of the nearest n neighbouring sampling points Xj. The neighbours are weighted according their distance Dij to the position Xi, controlled by the exponent p (2).
⎧ n −p ⎪ ∑ Dij X j ⎪⎪ j =1n , für Dij > 0 Xi = ⎨ −p Dij ⎪ ∑ j =1 ⎪ , für Dij = 0 ⎪⎩ X j
IV. CONCLUSIONS A major problem of mine clearance using metal detectors is the high false alarm rate, which is caused primarily by soil inhomogeneity and harmless metal parts, so called clutter. The key to significant lowering of the false alarm rate is the usage of a priori information about the types of mines in the field and the usage of position referenced multivariate measurement data.
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The visualization of the local-distributed sensor data gives an overview about distribution of the detected metal pieces. The classification – mine of ‘type x’ or ‘clutter’ – of a signature, also in the presence of uncooperative soil, is possible with usage of feature maintaining soil compensation.
The exponent is typical p = 2. Smaller values lead to smother images, while large values causing flat tops surrounding the sample points.
For a handheld metal detector, the ultrasonic 3D-referencesystem provides the resolution needed for the acquisition of mine signatures. But the external device used in the experimental setup should be scaled down to be handier.
For automatic pin pointing the region of interest and 2Dvisualisation, the soil compensation is applied on the gridded raw data (according to section II.A). Fig. 6 shows the track of the sensor movements and the visualisation past position correction, hazard rejection, gridding and MSR maximizing soil compensation for three different objects.
REFERENCES [1]
C. A. Bruschini, “A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining”, Belgium 2002 [2] H. Krüger, H. Ewald, Th. Krüger, S. Schulze, U. van Rienen, H.-W. Glock, “Appliance of imaging methods for metal detection in the humanitarian mine clearance” (in German), proceedings – DGZfP Annual conference of the German NDT organisation, Germany 2005 [3] H. Ewald, H. Krüger, “Inductive sensors and their application in metal detection”, International Conference on Sensing Technology, New Zealand 2005 [4] B. C. Weaver, R. J. Podhrasky, A. Nemat, “Metal detector for identifying target electrical characteristics, depth and size”, patent number: US 5,786,696, 1998 [5] A. G. Ranade, “Some uses of spectral methods”, University of Technology Hamburg-Harburg, 2004 [6] H. Krüger; .H. Ewald, “Image Processing and Pattern Recognition of Metal Detector Data”, IEEE-ICONIC 2007 - 3rd International Conference of Near-Field Characterization & Imaging, p. 285-289, St. Louis, USA 2007 [7] H. Krüger, H. Ewald, Th. Fechner, S. Bergeler, “Advanced signal processing for reduction of false alarm rate of Metal detectors for humanitarian mine clearance”, IEEE-IMTC 2006 – Instrumentation and Measurement Technology Conference Sorrento, p. 1452-1456, Italy 2006 [8] M. Sato, K. Takahashi, J. Fujiwara, “Development of the Hand held dual sensor ALIS and its evaluation”, 4th International Workshop on Advanced Ground Penetrating Radar, p. 3-7, 2007 [9] S. Bergeler, H. Krambeer, „Novel optical spatial filtering methods based on two-dimensional photodetector arrays“, Measurement science & technology, vol. 15, no. 7, p. 1309-1315, 2004 [10] D. Shepard, „A two-dimensional interpolation for irregularly-spaced data“, Proceedings of the ACM National Conference 1968, p. 517– 524, 1968
The pinpointing is done considering the symmetry and amplitude of the signature. The line over the signature maximum is also visualised as phase loop representation (but under usage of the feature maintaining soil compensation). The classification (a mine or not a mine of type x) actual implemented is based only on the phase information according to section II.B. To determine also the depth of a mine, the amplitude and/or signature wide should be used additionally.
Figure 6. Acquired measurements presented the deminer during scanning process (top), 2D and phase-loop representation of the processed data (bottom).
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