Detecting buried explosive hazards with handheld GPR and deep learning

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 9823, Issue , 2016, Pages

  Besaw, Lance E ^a  

^a Applied Research Inc   (United States)

Author keywords

Anomaly detection; Buried explosive hazard; Convolutional neural networks; Deep learning; Ground penetrating radar; Signal processing

Indexed keywords

AUTOMATIC TARGET RECOGNITION; CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; EXPLOSIVES; EXPLOSIVES DETECTION; GEOLOGICAL SURVEYS; GROUND PENETRATING RADAR SYSTEMS; HAZARDS; NEURAL NETWORKS; PATTERN RECOGNITION; RADAR; SIGNAL DETECTION; SIGNAL PROCESSING; SPEECH RECOGNITION; TRACKING RADAR;

ANOMALY DETECTION; BURIED EXPLOSIVE HAZARDS; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; GROUND PENETRATING RADAR;

LANDMINE DETECTION;

EID: 84982141319     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2223797     Document Type: Conference Paper

References (20)

1. Geneva International Centre for Humanitarian Demining, "Mines other than anti-personnel mines, " (March 2012).
2. Joint Improvised Explosive Device Defeat Organization, "Annual report, " (2010).
3. Ngan, P., Burke, S., Cresci, R., Wilson, J. N., Gader, P., Ho, K. C., Bartosz, E. and Duvoisin, H., "Development of region processing algorithms for HSTAMIDS: Status and field test results, " in Proc. SPIE Detection and Remediation Technologies for Mines and Minelike Targets X (2007).
4. Ho, K. C. and Gader, P. D., "A linear prediction land mine detector algorithms for handheld ground penetrating radar, " IEEE Trans. Geosci. Remote Sensing, 40(6), pp. 1374-1384 (2002).
5. Ho, K. C., Gader, P., Wilson, J. N. and Glenn, T. C., "Landmine detection using frequency domain features from GPR measurements and their fusion with time domain features, " Development of region processing algorithms for HSTAMIDS: Status and field test results, " in Proc. SPIE Detection and Remediation Technologies for Mines and Minelike Targets X (2005).
6. Wilson, J. N., Gader, P. D., Ho, K. C., Lee, W. H., Stanely, R. J. and Glenn, T. C., "Region processing of ground penetrating radar and electromagnetic induction for handheld landmine detection" in Proc. SPIE Detection and Remediation Technologies for Mines and Minelike Targets IX (2004).
7. Besaw, L.E. and Stimac, P.J., "Deep Convolutional Neural Networks for Classifying GPR B-Scans, " in Proc. SPIE Defense, Security and Sensing (2015).
8. Bengio, Y., Courville, A. and Vincent, P., "Unsupervised feature learning and deep learning: A review and new perspectives, " arXiv preprint arXiv:1206.5538v1 (2012).
9. Taigman, Y., Ming, Y., Ranzato, M.A. and Wolf, L., "DeepFace: Closing the gap to human-level performance in face verification, " In Computer Vision and Pattern Recognition (2014).
10. Ioffe, S. and Szegedy, C., "Batch normalization: accelerating deep network training by reducing internal covariate shift, " arXiv: 1502.03167 (2015).
11. Hannun, A., Case, C., Casper, J., et al., "Deep speech: Scaling up end-to-end speech recognition, " arXiv:1412.5567 (2014).
12. Lecun, Y. and Bengio, Y., "Convolutional networks for images, speech and time series, " The handbook of brain theory and neural networks 3361 (1995).
13. Besaw, L.E. and Stimac, P.J., "Deep Learning Algorithms for Detecting Explosive Hazards in Ground Penetrating Radar Data, " in Proc. SPIE Defense, Security and Sensing (2014).
14. Frigui H. and Gader, P., "Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a possibilistic k-nearest neighbor classifier, " Fuzzy Systems, IEEE Transactions on 17(1), pp. 185-199 (2009).
15. Torrione, P.A., Morton, K.D., Sakaguchi, R. and Collins, L.M., "Histograms of orientated gradients for landmine detection in ground-penetrating radar data, " IEEE Trans. On Geoscience and Remote Sensing 52 (3), (2014).
16. Hinton, G., Srivastava, N., Krizhevski, A., Sutskever, I., and Salakhutdinov, R., "Improving neural networks by preventing co-adaption of feature detectors, " arXiv: 1207.0580v1 (2012).
17. Abadi, M., Agarwal, A., Barham, P., et al., "TensorFlow: Large-scale machine learning on heterogeneous systems, " (2015). Software available from tensorflow.org.
18. Banko, M. and Brill, E., "Scaling very large corpoa for natural language disambiguation, " Proc. of the Association for Computational Linguistics (2001).
19. Geman, S., Bienenstock, E. and Doursat, R. "Neural networks and the bias/variance dilemma." Neural Computation 4: 1-58. doi:10.1162/neco.1992.4.1 (1992).
20. Ramanna, S., Lakhmi, J.C. and Howlett, R.J., Emerging Paradigms in Machine Learning, Springer, New York (2013).