抛撒地雷的夜视智能探测方法研究

王驰; 于明坤; 杨辰烨; 李思远; 李富迪; 李金辉; 方东; 栾信群

doi:10.37188/CO.2020-0214

Volume 14 Issue 5

Sep. 2021

Turn off MathJax

Article Contents

Chinese Optics > 2021 > 14(5): 1202-1211.

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

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

Citation:

PDF( 5045 KB)

Night vision intelligent detection method of scatterable landmines

doi: 10.37188/CO.2020-0214

1.
Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China
2.
Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China

Funds: Supported by National Natural Science Foundation of China (No. 41704123, No. 61773249); Science and Technology on Near-Surface Detection Laboratory (No. TCGZ2020C003)

More Information

Corresponding author: xinqun_luan@126.com
Received Date: 22 Dec 2020
Rev Recd Date: 14 Jan 2021

Available Online: 27 Mar 2021

Publish Date: 18 Sep 2021

Abstract

Abstract

Night vision intelligent detection method of scatterable landmines based on machine learning is presented. Firstly, the intelligent detection network model of scatterable landmines is designed and optimized based on the YOLO series algorithm. Then, the model measuring the distance between scatterable landmines and detection equipment is proposed based on the similarity principle of geometric optical imaging. Finally, a night vision intelligent detection system for scatterable landmines is built, tested and analyzed. The experimental results show that the optimized intelligent detection network model can detect scatterable landmines with an accuracy of 98.97%, a recall rate of 99.22%, and a mean average accuracy of 99.2%. Under the given experimental conditions, the optimized scatterable landmine ranging model has an error of ±10 cm in the calculated distance of scatterable landmines. The study shows that machine learning can perform intelligent and long-distance detection of scatterable landmines.
- scatterable mines detection,
- machine learning,
- low-light-level night vision,
- monocular ranging

FullText(HTML)

References(33)

References

[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