| Citation: | WANG Chi, YU Ming-kun, YANG Chen-ye, LI Si-yuan, LI Fu-di, LI Jin-hui, FANG Dong, LUAN Xin-qun. Night vision intelligent detection method of scatterable landmines[J]. Chinese Optics, 2021, 14(5): 1202-1211. doi: 10.37188/CO.2020-0214
|
| [1] |
DANIELS D J. A review of GPR for landmine detection[J]. Sensing and Imaging:An International Journal, 2006, 7(3): 90-123. doi: 10.1007/s11220-006-0024-5
|
| [2] |
LIANG F L, ZHANG H H, WANG Y M, et al.. Landmine-enhanced imaging based on migratory scattering in ultra-wideband synthetic aperture radar[C]. Proceedings of 2013 IEEE International Conference on Signal Processing, Communication and Computing, IEEE, 2013: 1-4.
|
| [3] |
KASBAN H, ZAHRAN O, ELARABY S M, et al. A comparative study of landmine detection techniques[J]. Sensing and Imaging:An International Journal, 2010, 11(3): 89-112. doi: 10.1007/s11220-010-0054-x
|
| [4] |
ŠIPOŠ D, GLEICH D. A lightweight and low-power UAV-borne ground penetrating radar design for landmine detection[J]. Sensors, 2020, 20(8): 2234. doi: 10.3390/s20082234
|
| [5] |
BAUR J, STEINBERG G, NIKULIN A, et al. Applying deep learning to automate UAV-based detection of scatterable landmines[J]. Remote Sensing, 2020, 12(5): 859. doi: 10.3390/rs12050859
|
| [6] |
KOSITSKY J, COSGROVE R, AMAZEEN C A, et al. Results from a forward-looking GPR mine detection system[J]. Proceedings of SPIE, 2002, 4742: 2002.
|
| [7] |
MONTIEL-ZAFRA V, CANADAS-QUESADA F J, VERA-CANDEAS P, et al. A novel method to remove GPR background noise based on the similarity of non-neighboring regions[J]. Journal of Applied Geophysics, 2017, 144: 188-203. doi: 10.1016/j.jappgeo.2017.07.010
|
| [8] |
TAKAHASHI Y, MISAWA T, MASUDA K, et al. Development of landmine detection system based on the measurement of radiation from landmines[J]. Applied Radiation and Isotopes, 2010, 68(12): 2327-2334. doi: 10.1016/j.apradiso.2010.03.021
|
| [9] |
BROSINSKY C A, EIRICH R, DIGNEY B L, et al. The application of telematics in the Canadian landmine detection capability[J]. IFAC Proceedings Volumes, 2001, 34(9): 227-233. doi: 10.1016/S1474-6670(17)41710-1
|
| [10] |
FREELAND R S, MILLER M L, YODER R E, et al. Forensic application of FM-CW and pulse radar[J]. Journal of Environmental and Engineering Geophysics, 2003, 8(2): 97-103. doi: 10.4133/JEEG8.2.97
|
| [11] |
NICKEL U, CHAUMETTE E, LARZABAL P. Estimation of extended targets using the generalized monopulse estimator: extension to a mixed target model[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 2084-2096. doi: 10.1109/TAES.2013.6558043
|
| [12] |
CHRZANOWSKI K. Review of night vision technology[J]. Opto-Electronics Review, 2013, 21(2): 153-181.
|
| [13] |
BOURREE L E. Performance of PHOTONIS’ low light level CMOS imaging sensor for long range observation[J]. Proceedings of SPIE, 2014, 9100: 910004.
|
| [14] |
GROSS E, GINAT R, NESHER O. Low light level CMOS sensor for night vision systems[J]. Proceedings of SPIE, 2015, 9541: 945107.
|
| [15] |
GADE R, MOESLUND T B. Thermal cameras and applications: a survey[J]. Machine Vision and Applications, 2014, 25(1): 245-262. doi: 10.1007/s00138-013-0570-5
|
| [16] |
LIU L, OUYANG W L, WANG X G, et al. Deep learning for generic object detection: a survey[J]. International Journal of Computer Vision, 2020, 128(2): 261-318. doi: 10.1007/s11263-019-01247-4
|
| [17] |
GIRSHICK R. Fast R-CNN[C]. Proceedings of 2015 IEEE International Conference on Computer Vision, IEEE, 2015: 1440-1448.
|
| [18] |
LIU W, ANGUELOV D, ERHAN D, et al.. SSD: single shot MultiBox detector[C]. Proceedings of the 14th European Conference on Computer Vision, Springer, 2016: 21-37.
|
| [19] |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 6517-6525.
|
| [20] |
FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. doi: 10.1109/TPAMI.2009.167
|
| [21] |
LU SH Y, WANG B ZH, WANG H J, et al. A real-time object detection algorithm for video[J]. Computers &Electrical Engineering, 2019, 77: 398-408.
|
| [22] |
HAMMAM A A, SOLIMAN M M, HASSANIEN A E. Real-time multiple spatiotemporal action localization and prediction approach using deep learning[J]. Neural Networks, 2020, 128: 331-344. doi: 10.1016/j.neunet.2020.05.017
|
| [23] |
DOU Q, CHEN H, YU L Q, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(7): 1558-1567. doi: 10.1109/TBME.2016.2613502
|
| [24] |
DONG ZH, WU Y W, PEI M T, et al. Vehicle type classification using a semisupervised convolutional neural network[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2247-2256. doi: 10.1109/TITS.2015.2402438
|
| [25] |
DIRO A A, CHILAMKURTI N. Distributed attack detection scheme using deep learning approach for Internet of Things[J]. Future Generation Computer Systems, 2018, 82: 761-768. doi: 10.1016/j.future.2017.08.043
|
| [26] |
FRIGUI H, ZHANG L J, GADER P, et al. An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination[J]. Information Fusion, 2012, 13(2): 161-174. doi: 10.1016/j.inffus.2009.10.001
|
| [27] |
KACHACH R, CAÑAS J M. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera[J]. Journal of Electronic Imaging, 2016, 25(3): 033021. doi: 10.1117/1.JEI.25.3.033021
|
| [28] |
HE K M, ZHANG X Y, REN SH Q, et al.. Deep residual learning for image recognition[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 770-778.
|
| [29] |
SMIRNOV E A, TIMOSHENKO D M, ANDRIANOV S N. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks[J]. AASRI Procedia, 2014, 6: 89-94. doi: 10.1016/j.aasri.2014.05.013
|
| [30] |
ITAKURA K, HOSOI F. Automatic tree detection from three-dimensional images reconstructed from 360° spherical camera using YOLO v2[J]. Remote Sensing, 2020, 12(6): 988. doi: 10.3390/rs12060988
|
| [31] |
LECHGAR H, BEKKAR H, RHINANE H. Detection of cities vehicle fleet using Yolo V2 and aerial images[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2019, XLII-4/W12: 121-126. doi: 10.5194/isprs-archives-XLII-4-W12-121-2019
|
| [32] |
KIM C, KIM H M, LYUH C G, et al.. Implementation of Yolo-v2 image recognition and other testbenches for a CNN accelerator[C]. Proceedings of 2019 IEEE 9th International Conference on Consumer Electronics, IEEE, 2019: 242-247.
|
| [33] |
WANG L H, YANG Y, SHI J CH. Measurement of harvesting width of intelligent combine harvester by improved probabilistic Hough transform algorithm[J]. Measurement, 2020, 151: 107130. doi: 10.1016/j.measurement.2019.107130
|
Figures(9) / Tables(4)
DownLoad:
