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A comprehensive review on landmine detection using deep learning techniques in 5G environment: open issues and challenges

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Abstract

Detection of Landmines, especially anti-tank mines, bombs, and unexploded substances, is one of the major challenges facing humanity. The devastation and human tragedy associated with undetected explosives are self-evident in war-torn communities. To deal with this problem, we are only left with proactive measures that such substances must be detected and dealt with before the fallout. Most available solutions have major shortcomings, such as cost, efficiency, and accuracy, where the trade-offs among them are inversely related. On the other hand, advances in deep learning, unmanned aerial vehicle, and sensing are making their way as potential technologies to revolutionize the detection and removal of landmines. In this paper, we go through the literature reviewing the most recent work featuring computerized technologies to detect landmines. To our knowledge, no such study has taken place in this respect. Our aim is to find out how deep learning can be integrated with landmine detection. We identify open challenges toward viable automated solutions that enable deep learning to optimize performance effectively.

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Referencese

  1. Robledo L, Carrasco M, Mery D (2009) A survey of land mine detection technology. Int J Remote Sens 30(9):2399–2410

    Article  Google Scholar 

  2. Merriam-Webster (2021) Landmine. Merriam-Webster.com Dictionary, June 2021, https://www.merriam- webster.com/dictionary/land

  3. U. Nations, Demining, Mayback Machine, May 2021, http://www.un.org/en/sections/issues- depth/demining/index.html

  4. L. M. (Report), “Casualties,” International Campaign for the Banning of Landmines, March 2017.

  5. Keeley R (2017) Improvised explosive devices (IED): a human- itarian mine action perspective. J Conv Weapons Destr 21(1):3

    Google Scholar 

  6. Hafiz AM, Bhat GM (2020) A survey on instance segmen- tation: state of the art. Int J Multimed Inf Retr 9(3):171–189

    Article  Google Scholar 

  7. Fernández MG, López YÁ, Arboleya AA, Valdés BG, Vaqueiro YR, Andrés FL-H, García AP (2018) Synthetic aperture radar imaging system for landmine detection using a ground penetrating radar on board a unmanned aerial vehicle. IEEE Access 6:45100–45112

    Article  Google Scholar 

  8. Osco LP, Junior JM, Ramos APM, deCastroJorge LA, Fatholahi SN, deAndradeSilva J, Matsubara ET, Pistori H, Gonçalves WN, Li J (2021) “A review on deep learning in UAV remote sensing. Int J Appl Earth Observ Geoinf 102:102456

    Google Scholar 

  9. Mozaffari M, Saad W, Bennis M, Nam Y-H, Debbah M (2019) A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Commun Surv Tutor 21(3):2334–2360

    Article  Google Scholar 

  10. Mohamed N, Al-Jaroodi J, Jawhar I, Noura H, Mahmoud S (2017) Uavfog: a UAV-based fog computing for internet of things. In: 2017 IEEE SmartWorld, ubiquitous intelligence and computing, advanced and trusted computed, scalable computing and communications, cloud and big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017, pp 1–8

  11. Suganthi G, Korah R (2014) Discrimination of mine-like objects in infrared images using artificial neural network. Indian J Appl Res 4(12):206–208

    Google Scholar 

  12. M. Bajić, (2021) Modeling and simulation of very high spatial resolution uxos and landmines in a hyperspectral scene for uav survey. Remote Sensing 13(5):837

    Article  Google Scholar 

  13. Yuksel SE, Bolton J, Gader P (2015) Multiple-instance hidden markov models with applications to landmine detection. IEEE Trans Geosci Remote Sens 53(12):6766–6775

    Article  Google Scholar 

  14. Yuksel SE, Gader PD (2016) Context-based classification via mixture of hidden markov model experts with applications in landmine detection. IET Comput Vis 10(8):873–883

    Article  Google Scholar 

  15. Makki I, Younes R, Francis C, Bianchi T, Zucchetti M (2017) A Survey of Landmine Detection Using Hyperspectral Imag- ing. ISPRS J Photogramm Remote Sens 124:40–53

    Article  Google Scholar 

  16. Bestagini P, Lombardi F, Lualdi M, Picetti F, Tubaro S (2020) Landmine detection using autoencoders on multipolarization gpr volumetric data. IEEE Trans Geosci Remote Sens 59(1):182–195

    Article  Google Scholar 

  17. Silva JS, Guerra IFL, Bioucas-Dias J, Gasche T (2019) Landmine detection using multispectral images. IEEE Sens J 19(20):9341–9351

    Article  Google Scholar 

  18. Gürkan S, Karapınar M, Doğan S (2019) Detection and imaging of underground objects for distinguishing explosives by using a fluxgate sensor array. Appl Sci 9(24):5415

    Article  Google Scholar 

  19. Rafique W, Zheng D, Barras J, Joglekar S, Kosmas P (2019) Predictive analysis of landmine risk”. IEEE Access 7:107259–107269

    Article  Google Scholar 

  20. Tong Z, Gao J, Yuan D (2020) Advances of deep learning applications in ground-penetrating radar: a survey. Construction and Build Mater 258:120371

    Article  Google Scholar 

  21. Baur J, Steinberg G, Nikulin A, Chiu K, de Smet TS (2020) Applying deep learning to automate uav-based detection of scatterable landmines. Remote Sens 12(5):859

    Article  Google Scholar 

  22. Safatly L, Baydoun M, Alipour M, Al-Takach A, Atab K, Al-Husseini M, El-Hajj A, Ghaziri H (2021) Detection and classification of landmines using machine learning applied to metal detector data. J Exp Theor Artif Intell 33(2):203–226

    Article  Google Scholar 

  23. Priya CN, Ashok SD, Maji B, Kumaran KS (2021) Deep learning based thermal image processing approach for detection of buried objects and mines. Eng J 25(3):61–67

    Article  Google Scholar 

  24. Girshick R, Donahue J, Darrell T, Malik J (2015) Region- based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158

    Article  Google Scholar 

  25. Bello R (2013) Literature review on landmines and detection meth- ods. Front Sci 3(1):27–42

    Google Scholar 

  26. Habib MK (2007) Controlled biological and biomimetic sys- tems for landmine detection. Biosens Bioelectron 23(1):1–18

    Article  MathSciNet  Google Scholar 

  27. Sargisson RJ, McLean IG, Brown J, Bach H (2012) Environmental determinants of landmine detection by dogs: findings from a large-scale study in Afghanistan. 16:74–81

  28. Poling A, Weetjens BJ, Cox C, Beyene NW, Sully A (2010) Using giant african pouched rats (Cricetomys gambianus) to detect landmines. Psychol Record 60(4):715–728

    Article  Google Scholar 

  29. Masunaga S, Nonami K (2007) Controlled metal detector mounted on mine detection robot. Int J Adv Rob Syst 4(2):26

    Article  Google Scholar 

  30. Achal S, Mcfee J, Howse J (2010) Gradual dispersal of explosives by ants and its possible implication for smart- landmine production. In: International Symposium of Human- itarian Demining, Šibenik, Croatia, pp 60–65

  31. Yagur-Kroll S, Lalush C, Rosen R, Bachar N, Moskovitz Y, Belkin S (2014) Escherichia coli bioreporters for the detection of 2, 4-dinitrotoluene and 2, 4, 6-trinitrotoluene. Appl Microbiol Biotechnol 98(2):885–895

    Article  Google Scholar 

  32. Kasban H, Zahran O, Elaraby SM, El-Kordy M, Abd El-Samie FE (2010) A comparative study of landmine detection techniques. Sens Imaging Int J 11(3):89–112

    Article  Google Scholar 

  33. Cardona Rendón L, Jiménez Builes JA, Vane-gas Molina NA (2014) Landmine detection technologies to face the demining problem in antioquia. Dyna; 81(183)

  34. Dula J, Zare A, Ho D, Gader P (2013) Landmine classifi- cation using possibilistic k-nearest neighbors with wideband electromagnetic induction data. In: Detection and sensing of mines, explosive objects, and obscured targets XVIII, vol. 8709. International Society for Optics and Photonics, p 87091F

  35. Kaneko AM, Fukushima E, Endo G (2014) A discrimination method for landmines and metal fragments using metal detectors. J Conv Weapons Destr 18(1):16

    Google Scholar 

  36. Kruger H, Ewald H (2008) Handheld metal detector with online visualisation and classification for the humanitarian mine clearance. In SENSORS, Lecce, Italy. IEEE, pp 415–418

  37. Collins L, Gao P, Makowsky L, Moulton J, Reidy D, Weaver D (2000) Improving detection of low-metallic content landmines using EMI data. In: IGARSS 2000. IEEE 2000 inter- national geoscience and remote sensing symposium. taking the pulse of the planet: the role of remote sensing in managing the environment. Proceedings (Cat. No. 00CH37120), vol. 4. IEEE, pp 1631–1633

  38. Mazhar R, Gader PD, Wilson JN (2009) Matching-pursuits dissimilarity measure for shape-based comparison and classifi- cation of high-dimensional data. IEEE Trans Fuzzy Syst 17(5):1175–1188

    Article  Google Scholar 

  39. Tantum SL, Collins LM (2001) A comparison of algorithms for subsurface target detection and identification using time- domain electromagnetic induction data. IEEE Trans Geosci Remote Sens 39(6):1299–1306

    Article  Google Scholar 

  40. Tran MD-J, Abeynayake C, Jain LC (2011) A target discrimination methodology utilizing wavelet-based and morphological feature extraction with metal detector array data. IEEE Trans Geosci Remote Sens 50(1):119–129

    Article  Google Scholar 

  41. Bruschini C, Gros B, Guerne F, Pièce P-Y, Carmona O (1998) Ground penetrating radar and imaging metal detector for antipersonnel mine detection. J Appl Geophys 40(1–3):59–71

    Article  Google Scholar 

  42. Nguyen TT, Hao DN, Lopez P, Cremer F, Sahli H (2005) Thermal infrared identification of buried landmines. In: De tection and remediation technologies for mines and Minelike targets X, vol 5794. International Society for Optics and Photonics, pp 198–208

  43. Richards JA, Jia X (2005) The effect of the atmosphere on radiation. In: Remote sensing digital image analysis: an introduction, Canbeerra, June 2005, pp. 28–34.

  44. Kaya S, Leloglu UM (2017) Buried and surface mine detec- tion from thermal image time series. IEEE J Sel Top Appl Earth Observ Remote Sens 10(10):4544–4552

    Article  Google Scholar 

  45. Janssen YH, de Jong AN, Winkel H, van Putten FJ (1996) Detection of surface-laid and buried mines with IR and CCD cameras: an evaluation based on measurements. In: Detection and remediation technologies for mines and minelike targets, vol 2765. International Society for Optics and Photonics, pp 448–459

  46. Ederra GB (1999) Mathematical morphology techniques applied to anti-personnel mine detection. Ph.D. dissertation, MS The- sis, Department of Electronics and Information Processing, vol 8, pp 1–6

  47. Thành NT, Hào DN, Sahli H (2009) Infrared thermography for land mine detection. In: Augmented vision perception in infrared. Springer, pp 3–36

  48. Van Kempen L, Kaczmarec M, Sahli H, Cornelis J (1998) Dynamic infrared image sequence analysis for anti-personnel mine detection. In: IEEE benelux signal processing chap ter, signal processing symposium; leuven, belgium. ieee benelux signal processing chapter, signal processing sym posium, pp. 215–218

  49. Thanh NT, Sahli H, Hao DN (2007) Finite-difference methods and validity of a thermal model for landmine detection with soil property estimation. IEEE Trans Geosci Remote Sens 45(3):656–674

    Article  Google Scholar 

  50. Ajlouni A, Sheta A (2008) Landmind detection with IR sensors using karhunen loeve transformation and watershed segmen-tation. In: 5th international multi-conference on systems, signals and devices, Amman, Jordan. IEEE, pp 1–6

  51. Sendur IK, Baertlein BA (2000) Numerical simulation of thermal signatures of buried mines over a diurnal cycle. Iin: Detection and remediation technologies for mines and minelike targets V, vol 4038. International Society for Optics and Photonics, pp 156–167

  52. Paik J, Lee CP, Abidi MA (2002) Image processing-based mine detection techniques: a review. Subsurf Sens Technol Appl 3(3):153–202

    Article  Google Scholar 

  53. Ko KH, Jang G, Park K, Kim K (2012) GPR-based land- mine detection and identification using multiple features. Int J Antennas Propag, Article ID 826404

  54. Becker J, Havens TC, Pinar A, Schulz TJ (2015) Deep belief networks for false alarm rejection in forward-looking ground-penetrating radar. In: Detection and sensing of mines, explosive objects, and obscured targets XX, vol 9454. In ternational Society for Optics and Photonics, p 94540W

  55. Besaw LE, Stimac PJ (2015) Deep convolutional neural networks for classifying GPR B-scans. In: Detection and sensing of mines, explosive objects, and obscured targets XX, vol 9454. SPIE, pp 385–394

  56. Giannakis I, Giannopoulos A, Warren C, Davidson N (2015) Numerical modelling and neural networks for landmine de-tection using ground penetrating radar. In: 8th international workshop on advanced ground penetrating radar (IWAGPR), Florence, Italy. IEEE, pp 1–4

  57. Dou Q, Wei L, Magee DR, Cohn AG (2016) Realtime hyperbola recognition and fitting in GPR data. IEEE Trans Geosci Remote Sens 55(1):51–62

    Article  Google Scholar 

  58. Lameri S, Lombardi F, Bestagini P, Lualdi M, Tubaro S (2017) Landmine detection from GPR data using convolutional neural networks. In: 25th European signal processing conference (EUSIPCO), Kos, Greece. IEEE, pp 508–512

  59. Kim N, Kim S, An Y-K, Lee J-J (2021) A novel 3d gpr im- age arrangement for deep learning-based underground object classification. Int J Pavement Eng 22(6):740–751

    Article  Google Scholar 

  60. Pham M-T, Lefèvre S (2018) Buried object detection from b-scan ground penetrating radar data using faster-RCNN. In: IGARSS 2018–2018 IEEE international geoscience and re- mote sensing symposium. IEEE, pp 6804–6807

  61. Ren S, He K, Girshick R, Sun J (2015) Faster r-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst, 28

  62. Warren C, Giannopoulos A, Giannakis I (2016) gprmax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput Phys Commun 209:163–170

    Article  Google Scholar 

  63. Ozkaya U, Melgani F, Bejiga MB, Seyfi L, Donelli M (2020) Gpr b scan image analysis with deep learning methods. Measurement 165:107770

    Article  Google Scholar 

  64. Pambudi AD, Fauß M, Ahmad F, Zoubir AM (2020) Minimax robust landmine detection using forward-looking ground-penetrating radar. IEEE Trans Geosci Remote Sens 58(7):5032–5041

    Article  Google Scholar 

  65. K.Ishitsuka, S. Iso, K. Onishi, and T. Matsuoka, “Object detection in ground-penetrating radar images using a deep convolutional neural network and image set preparation by migration,” International Journal of Geophysics, vol. 2018, 2018.

  66. Dinh K, Gucunski N, Duong TH (2018) An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks. Autom Constr 89:292–298

    Article  Google Scholar 

  67. Lei W, Hou F, Xi J, Tan Q, Xu M, Jiang X, Liu G, Gu Q (2019) Automatic hyperbola detection and fitting in GPR B- scan image. Autom Constr 106:102839

    Article  Google Scholar 

  68. Alvarez JK, Kodagoda S (2018) Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring. In: 2018 13th IEEE conference on industrial electronics and applications (ICIEA). IEEE, pp 611–616

  69. Picetti F, Testa G, Lombardi F, Bestagini P, Lualdi M, Tubaro S (2018) Convolutional autoencoder for landmine detection on GPR scans. In: 2018 41st international conference on telecommunications and signal processing (TSP). IEEE, pp 1–4

  70. Tong Z, Gao J, Zhang H (2017) Recognition, location, mea- surement, and 3d reconstruction of concealed cracks using convolutional neural networks. Constr Build Mater 146:775–787

    Article  Google Scholar 

  71. Manolakis D, Shaw G (2002) Detection algorithms for hyperspectral imaging applications. IEEE Signal Process Mag 19(1):29–43

    Article  Google Scholar 

  72. Manolakis D, Truslow E, Pieper M, Cooley T, Brueggeman M (2013) Detection algorithms in hyperspectral imaging systems: an overview of practical algorithms. IEEE Signal Process Mag 31(1):24–33

    Article  Google Scholar 

  73. Axelsson M, Friman O, Haavardsholm TV, Renhorn I (2016) Target detection in hyperspectral imagery using forward modeling and in-scene information. ISPRS Journal of Photogram- metry and Remote Sensing 119:124–134

    Article  Google Scholar 

  74. Zhang L, Zhang L, Tao D, Huang X, Du B (2013) Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans Geosci Remote Sens 52(8):4955–4965

    Article  Google Scholar 

  75. Makki I, Younes R, Francis C, Bianchi T, Zucchetti M (2017) Classification algorithms for landmine detection using hyperspectral imaging. In: 2017 first international conference on landmine: detection, clearance and legislations (LDCL). IEEE, pp 1–6

  76. Scharf LL, McWhorter LT (1996) Adaptive matched sub- space detectors and adaptive coherence estimators. In: Confer ence record of the thirtieth Asilomar conference on signals, systems and computers. IEEE, pp 1114–1117

  77. Ren H, Du Q, Chang C-I, Jensen JO (2003) Comparison between constrained energy minimization based approaches for hyperspectral imagery. In: IEEE workshop on advances in techniques for analysis of remotely sensed data, 2003. IEEE, pp 244–248

  78. Harsanyi JC, Chang C-I (1994) Hyperspectral image classifi- cation and dimensionality reduction: An orthogonal subspace projection approach. IEEE Trans Geosci Remote Sens 32(4):779–785

    Article  Google Scholar 

  79. Wang L, Zhao C (2016) Hyperspectral image processing. Springer, Berlin

    Book  Google Scholar 

  80. Chang C-I (1999) Spectral information divergence for hyper- spectral image analysis. In: IEEE 1999 international geo- science and remote sensing symposium. IGARSS’99 (Cat. No. 99CH36293), vol 1. IEEE, pp 509–511

  81. Yin J, Wang Y, Wang Y, Zhao Z (2010) A modified algorithm for multi-target detection in hyperspectral image. In: IEEE 2nd international asia conference on informatics in control, automation and robotics (CAR 2010), vol 3, pp 105–108

  82. Golovin A, Demin A (2018) Optical-digital complex for detection of remote mines and mapping of minefields. In J Phys Conf Ser 1096(1):012011

    Article  Google Scholar 

  83. Khodor M, Makki I, Younes R, Bianchi T, Khoder J, Francis C, Zucchetti M (2021) Landmine detection in hy- perspectral images based on pixel intensity. Remote Sens Appl Soc Environ 21:100468

    Google Scholar 

  84. Cunliffe A, Brazier R, Anderson K (2016) Remote sensing of environment ultra-fi ne grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens Environ [Internet] 183:129–143

    Article  Google Scholar 

  85. Bhardwaj A, Sam L, Martín-Torres FJ, Kumar R et al (2016) UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sens Environ 175:196–204

    Article  Google Scholar 

  86. Ding M-L, Ding C-B, Tang L, Wang X-M, Qu J-M, Wu R (2020) A w-band 3-d integrated mini-SAR system with high imaging resolution on UAV platform. IEEE Access 8:113601–113609

    Article  Google Scholar 

  87. García-Fernández M, López YÁ, Arboleya A, González- Valdés B, Rodríguez-Vaqueiro Y, Gómez MEDC, Andrés FL-H (2017) Antenna diagnostics and characterization using unmanned aerial vehicles. IEEE Access 5:23563–23575

    Article  Google Scholar 

  88. Ismail A, Elmogy M, ElBakry H (2014) Landmines detection using autonomous robots: a survey. Int J Emerging Trends Technol Comput Sci 3(4):184–187

    Google Scholar 

  89. Castiblanco C, Rodriguez J, Mondragon I, Parra C, Colorado J (2014) Air drones for explosive landmines detection. In: ROBOT2013: first iberian robotics conference. Springer, pp 107–114

  90. Gavazzi B, Le Maire P, Munschy M, Dechamp A (2016) Fluxgate vector magnetometers: a multisensor device for ground, UAV, and airborne magnetic surveys. Lead Edge 35(9):795–797

    Article  Google Scholar 

  91. Sipos D, Planinsic P, Gleich D (2017) On drone ground penetrating radar for landmine detection. In: 2017 First inter- national conference on landmine: detection, clearance and legislations (LDCL). IEEE, pp 1–4

  92. Šipoš D, Gleich D (2020) A lightweight and low-power UAV- borne ground penetrating radar design for landmine detection. Sensors 20(8):2234

    Article  Google Scholar 

  93. Colorado J, Perez M, Mondragon I, Mendez D, Parra C, Devia C, Martinez-Moritz J, Neira L (2017) An integrated aerial system for landmine detection: SDR-based ground penetrating radar onboard an autonomous drone. Adv Robot 31(15):791–808

    Article  Google Scholar 

  94. Cerquera MRP, Montaño JDC, Mondragón I, Canbolat H (2017) Uav for landmine detection using SDR-based GPR technology. In: Robots operating in hazardous environments. IntechOpen, pp 26–55

  95. Burr R, Schartel M, Schmidt P, Mayer W, Walter T, Waldschmidt C (2018) Design and implementation of a FMCW GPR for UAV-based mine detection. In: 2018 IEEE MTT-S international conference on microwaves for intelligent mobility (ICMIM). IEEE, pp 1–4

  96. Reigber A, Moreira A (2000) First demonstration of airborne SAR tomography using multibaseline l-band data. IEEE Trans Geosci Remote Sens 38(5):2142–2152

    Article  Google Scholar 

  97. Krieger G, Hajnsek I, Papathanassiou KP, Younis M, Moreira A (2010) Interferometric synthetic aperture radar (SAR) missions employing formation flying. Proc IEEE 98(5):816–843

    Article  Google Scholar 

  98. Schartel M, Burr R, Bähnemann R, Mayer W, Wald- Schmidt C (2020) An experimental study on airborne landmine detec tion using a circular synthetic aperture radar. arXiv preprint arXiv:2005.02600, mMy

  99. Almutiry M (2020) UAV tomographic synthetic aperture radar for landmine detection. Eng Technol Appl Sci Res 10(4):5933–5939

    Article  Google Scholar 

  100. Alvey BJ, Anderson DT, Yang C, Buck A, Keller JM, Yasuda KE, Ryan HA (2021) Characterization of deep learning-based aerial explosive hazard detection using simulated data. In: 2021 IEEE symposium series on computational intelligence (SSCI). IEEE, 2, pp 1–8

  101. Shah S,Dey D, Lovett C, Kapoor A (2018) Airsim: high- fidelity visual and physical simulation for autonomous vehicles. In: Field and service robotics. Springer, pp 621–635

  102. Travassos XL, Avila SL, Ida N (2020) Artificial neural networks and machine learning techniques applied to ground penetrating radar: a review. Appl Comput Inf 17(2)

  103. Yoo L-S, Lee J-H, Lee Y-K, Jung S-K, Choi Y (2021) Application of a drone magnetometer system to military mine detection in the demilitarized zone. Sensors 21(9):3175

    Article  Google Scholar 

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Acknowledgements

This research work was funded by Institutional Fund Projects under grant no (IFPNC-001-611-2020). Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdelaziz University, Jeddah, Saudi Arabia.

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Authors and Affiliations

  1. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Ahmed Barnawi, Bander Alzahrani, Amal Almansour & Adeeb Noor

  2. School of Computer Science Engineering and Technology, Greater Noida, Uttar Pradesh, India

    Ishan Budhiraja

  3. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Krishan Kumar & Neeraj Kumar

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Barnawi, A., Budhiraja, I., Kumar, K. et al. A comprehensive review on landmine detection using deep learning techniques in 5G environment: open issues and challenges. Neural Comput & Applic 34, 21657–21676 (2022). https://doi.org/10.1007/s00521-022-07819-9

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