Abstract
The task of classifying unexploded mortars is critical in both humanitarian and military explosive ordnance disposal (EOD) operations. Classification needs to be completed quickly and accurately and is the first step toward disarming the ordnance because it provides information about the fuzing mechanism, or the stage in the arming cycle that the ordnance is currently in. To assist EOD technicians with mortar identification, this article presents an automated image-based algorithm and the database of images used in its development. The algorithm utilizes convolutional networks with variations to training to improve performance for ordnance found in varying states of disassembly. The classifier developed was found to be 98.5% accurate for these lab condition photos; future work will focus on more cluttered environments.
Issue Section:
Research Papers
References
1.
Bruschini
, C.
, 2001
, “Commercial Systems for the Direct Detection of Explosives for Explosive Ordnance Disposal Tasks
,” Subsurface Sens. Technol. Appl.
, 2
(3
), pp. 299
–336
. 2.
Axelsson
, M.
, Friman
, O.
, Johansson
, I.
, Nordberg
, M.
, and Ostmark
, H.
, 2013
, “Detection and Classification of Explosive Substances in Multispectral Image Sequences Using Linear Subspace Matching
,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
, Vancouver, BC, Canada
, May 26–31
, pp. 3492
–3496
.3.
Chen
, X.
, O Neill
, K.
, Barrowes
, B. E.
, Grzegorczyk
, T. M.
, and Kong
, J. A.
, 2004
, “Application of a Spheroidal-Mode Approach and a Differential Evolution Algorithm for Inversion of Magneto-Quasistatic Data in UXO Discrimination
,” Inverse Prob.
, 20
(6
), p. S27
. 4.
Prouty
, M.
, George
, D. C.
, and Snyder
, D. D.
, 2011
, “Metalmapper: A Multi-Sensor TEM System for UXO Detection and Classification,” Geometrics Inc.
, San Jose, CA
, Technical Report, Accession No. ADA578954.5.
McFee
, J. E.
, and Das
, Y.
, 1980
, “The Detection of Buried Explosive Objects
,” Can. J. Remote Sens.
, 6
(2
), pp. 104
–121
. 6.
Szeliski
, R.
, 2010
, Comuputer Vision: Algorithms and Applications
, Springer
, Berlin/Heidelberg, Germany
.7.
Zeiler
, M. D.
, and Fergus
, R.
, 2014
, “Visualizing and Understanding Convolutional Networks
,” European Conference on Computer Vision
, Zurich, Switzerland
, Sept. 6–12
, Springer, pp. 818
–833
.8.
Liu
, Q.
, and Liu
, C.
, 2017
, “A Novel Locally Linear KNN Method With Applications to Visual Recognition
,” IEEE Trans. Neural Netw. Learn. Syst.
, 28
(9
), pp. 2010
–2021
. 9.
Billings
, S. D.
, 2004
, “Discrimination and Classification of Buried Unexploded Ordnance Using Magnetometry
,” IEEE Trans. Geosci. Remote Sens.
, 42
(6
), pp. 1241
–1251
. 10.
Amer
, S.
, Shirkhodaie
, A.
, and Rababaah
, H.
, 2008
, “UXO Detection, Characterization, and Remediation Using Intelligent Robotic Systems
,” Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, Vol. 6953 of PROCSPIE
, Orlando, FL
, Mar. 17–20
.11.
Katartzis
, A.
, Vanhamel
, I.
, Chan
, J.
, and Sahli
, H.
, 2004
, “Remote Sensing Minefield Area Reduction: Model-Based Approaches for the Extraction of Minefield Indicators
,” Theory and Applications of Knowledge-Driven Image Information Mining With Focus on Earth Observation
, Madrid, Spain
, Mar. 17–18
.12.
Shirkhodaie
, A.
, and Rababaah
, H.
, 2007
, “Visual Detection, Recognition, and Classification of Surface-Buried UXO Based on Soft-Computing Decision Fusion
,” Detection and Remediation Technologies for Mines and Minelike Targets XII
, Orlando, FL
, Apr. 11–12
, p. 655328
.13.
LeCun
, Y.
, Boser
, B.
, Denker
, J. S.
, Henderson
, D.
, Howard
, R. E.
, Hubbard
, W.
, and Jackel
, L. D.
, 1989
, “Backpropagation Applied to Handwritten Zip Code Recognition
,” Neural Comput.
, 1
(4
), pp. 541
–551
. 14.
Venieris
, S. I.
, and Bouganis
, C.
, 2019
, “fpgaconvnet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAS
,” IEEE Trans. Neural Netw. Learn. Syst.
, 30
(2
), pp. 326
–342
. 15.
Kang
, Y.
, Chen
, S.
, Wang
, X.
, and Cao
, Y.
, 2019
, “Deep Convolutional Identifier for Dynamic Modeling and Adaptive Control of Unmanned Helicopter
,” IEEE Trans. Neural Netw. Learn. Syst.
, 30
(2
), pp. 524
–538
. 16.
Kim
, J.
, Nguyen
, A.
, and Lee
, S.
, 2019
, “Deep CNN-Based Blind Image Quality Predictor
,” IEEE Trans. Neural Netw. Learn. Syst.
, 30
(1
), pp. 11
–24
. 17.
Sakhavi
, S.
, Guan
, C.
, and Yan
, S.
, 2018
, “Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks
,” IEEE Trans. Neural Netw. Learn. Syst.
, 29
(11
), pp. 5619
–5629
. 18.
Liu
, N.
, Han
, J.
, Liu
, T.
, and Li
, X.
, 2018
, “Learning to Predict Eye Fixations Via Multiresolution Convolutional Neural Networks
,” IEEE Trans. Neural Netw. Learn. Syst.
, 29
(2
), pp. 392
–404
. 19.
LeCun
, Y.
, Kavukcuoglu
, K.
, and Farabet
, C.
, 2010
, “Convolutional Networks and Applications in Vision
,” Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS)
, Paris, France
, May 30–June 2
.20.
Krizhevsky
, A.
, Sutskever
, I.
, and Hinton
, G. E.
, 2012
, Advances in Neural Information Processing Systems 25
, F.
Pereira
, C. J. C.
Burges
, L.
Bottou
, and K. Q.
Weinberger
, eds., Curran Associates, Inc.
, Red Hook, NY
, pp. 1097
–1105
.21.
2021
, “La mine d argent de potosi: la technique au centre d une lutte de pouvoir entre les incas et les espagnols (xvie-xviie siècles)
.”22.
2020
, “Golden West Humanitarian Foundation
.”23.
Simonyan
, K.
, and Zisserman
, A.
, 2014
, “Very Deep Convolutional Networks for Large-Scale Image Recognition
,” CoRR
. https://arxiv.org/abs/1409.1556Copyright © 2022 by ASME
You do not currently have access to this content.
