Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models
<p/> <p>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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2005-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/ASP.2005.1867 |
| _version_ | 1818009144022532096 |
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| author | Ho KC Gader Paul Frigui Hichem |
| author_facet | Ho KC Gader Paul Frigui Hichem |
| author_sort | Ho KC |
| collection | DOAJ |
| description | <p/> <p>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 m <inline-formula><graphic file="1687-6180-2005-419248-i1.gif"/></inline-formula> 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%.</p> |
| first_indexed | 2024-04-14T05:38:20Z |
| format | Article |
| id | doaj.art-45e9d3489a8b45e19cb40674dfaba0d3 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-04-14T05:38:20Z |
| publishDate | 2005-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-45e9d3489a8b45e19cb40674dfaba0d32022-12-22T02:09:33ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802005-01-01200512419248Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov ModelsHo KCGader PaulFrigui Hichem<p/> <p>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 m <inline-formula><graphic file="1687-6180-2005-419248-i1.gif"/></inline-formula> 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%.</p>http://dx.doi.org/10.1155/ASP.2005.1867 |
| spellingShingle | Ho KC Gader Paul Frigui Hichem Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models EURASIP Journal on Advances in Signal Processing |
| title | Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models |
| title_full | Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models |
| title_fullStr | Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models |
| title_full_unstemmed | Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models |
| title_short | Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models |
| title_sort | real time landmine detection with ground penetrating radar using discriminative and adaptive hidden markov models |
| url | http://dx.doi.org/10.1155/ASP.2005.1867 |
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