Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models
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Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models Hichem Frigui Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA Email: [email protected]
K. C. Ho Department of Electrical and Computer Engineering, University of Missouri-Columbia, Columbia, MO 65211, USA Email: [email protected]
Paul Gader Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA Email: [email protected] Received 25 October 2004; Revised 3 March 2005; Recommended for Publication by Fulvio Gini We propose a real-time software system for landmine detection using ground-penetrating radar (GPR). The system includes an efficient and adaptive preprocessing component; a hidden Markov model- (HMM-) based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline). It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m2 of simulated dirt and gravel roads) for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%. Keywords and phrases: landmine detection, hidden Markov models, corrective training, adaptive preprocessing.
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INTRODUCTION
Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. It is estimated that more than 100 million landmines are buried in more than 80 countries around the world, and that 26 000 people, mostly civilians, a year are either killed or maimed by a landmine [1, 2]. The detection problem is compounded by the large variety of landmine types, differing soil conditions, temperature and weather conditions, and varying terrain, to name a few. Detection and removal of landmines is therefore a significant problem, and has attracted several researchers in recent years. One challenge in landmine detection lies in plastic or low metal mines that cannot or are difficult to detect by traditional metal detectors.
Varieties of sensors have been proposed or are under inves
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