Abstract
A prototype landmine detecting vehicle is presented. The vehicle is equipped with a Ground Penetrating Radar working in the frequency domain. The device collects 3D images defined over coordinates system: along track × across track × time, where the time (which can be associated with the depth) is obtained from frequency measurements via FFT. Learning of the detector is carried out by a boosting algorithm and is based on our proposition of 3D Haar-like features. Algorithmic details and experimental results are described, in particular: obtained accuracy, sensitivity, false-alarms rate and ROC curve.
Agreement no. 0091/R/TOO/2010/12 for R&D project no. 0 R00 0091 12, dated on 30.11.2010, signed with the Ministry of Science and Higher Education in Poland (consortium of Military Institute or Armament Technology in Zielonka and Autocomp Management Sp. z o.o.).
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Klęsk, P., Godziuk, A., Kapruziak, M., Olech, B. (2013). Landmine Detection in 3D Images from Ground Penetrating Radar Using Haar-Like Features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_51
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DOI: https://doi.org/10.1007/978-3-642-38658-9_51
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