{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:45:58Z","timestamp":1773888358766,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007209","name":"National Science and Technology Development Agency through the Thailand Graduate Institute of Science and Technology (TGIST)","doi-asserted-by":"publisher","award":["SCA-CO-2559-2297-TH"],"award-info":[{"award-number":["SCA-CO-2559-2297-TH"]}],"id":[{"id":"10.13039\/501100007209","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007209","name":"National Science and Technology Development Agency through the Thailand Graduate Institute of Science and Technology (TGIST)","doi-asserted-by":"publisher","award":["KMUTNB-64-KNOW-45"],"award-info":[{"award-number":["KMUTNB-64-KNOW-45"]}],"id":[{"id":"10.13039\/501100007209","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007345","name":"King Mongkut\u2019s University of Technology North Bangkok","doi-asserted-by":"publisher","award":["SCA-CO-2559-2297-TH"],"award-info":[{"award-number":["SCA-CO-2559-2297-TH"]}],"id":[{"id":"10.13039\/501100007345","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007345","name":"King Mongkut\u2019s University of Technology North Bangkok","doi-asserted-by":"publisher","award":["KMUTNB-64-KNOW-45"],"award-info":[{"award-number":["KMUTNB-64-KNOW-45"]}],"id":[{"id":"10.13039\/501100007345","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes the implementation of and experimentation with GPR for real-time automatic detection of buried IEDs. GPR, consisting of hardware and software, was implemented. A UWB antenna was designed and implemented, particularly for the operation of the GPR. The experiments were conducted in order to demonstrate the real-time automatic detection of buried IEDs using GPR with an R-CNN algorithm. In the experiments, the GPR was mounted on a pickup truck and a maintenance train in order to find the IEDs buried under a road and a railway, respectively. B-scan images were collected using the implemented GPR. R-CNN-based detection for the hyperbolic pattern, which indicates the buried IED, was performed along with pre-processing, for example, using zero offset removal, and background removal and filtering. Experimental results in terms of detecting the hyperbolic pattern in B-scan images were shown and verified that the proposed GPR system is superior to the conventional one using region analysis processing-based detection. Results also showed that pre-processing is required in order to improve and\/or clean the hyperbolic pattern before detection. The GPR can automatically detect IEDs buried under roads and railways in real time by detecting the hyperbolic pattern appearing in the collected B-scan image.<\/jats:p>","DOI":"10.3390\/s22228710","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:30:52Z","timestamp":1668400252000},"page":"8710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Implementation of and Experimentation with Ground-Penetrating Radar for Real-Time Automatic Detection of Buried Improvised Explosive Devices"],"prefix":"10.3390","volume":"22","author":[{"given":"Pachara","family":"Srimuk","sequence":"first","affiliation":[{"name":"Research Center of Innovation Digital and Electromagnetic Technology, Electrical Engineering, Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut\u2019s University of Technology North Bangkok, Bangkok 10800, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-7674","authenticated-orcid":false,"given":"Akkarat","family":"Boonpoonga","sequence":"additional","affiliation":[{"name":"Research Center of Innovation Digital and Electromagnetic Technology, Electrical Engineering, Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut\u2019s University of Technology North Bangkok, Bangkok 10800, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9540-4806","authenticated-orcid":false,"given":"Kamol","family":"Kaemarungsi","sequence":"additional","affiliation":[{"name":"National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathumthani 12120, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4092-8634","authenticated-orcid":false,"given":"Krit","family":"Athikulwongse","sequence":"additional","affiliation":[{"name":"National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathumthani 12120, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4015-543X","authenticated-orcid":false,"given":"Sitthichai","family":"Dentri","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering Technology, Collage of Industrial Technology, King Mongkut\u2019s University of Technology North Bangkok, Bangkok 10800, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1002\/2017RS006402","article-title":"Automatic Detection and Classification of Buried Objects Using Ground-Penetrating Radar for Counter-Improvised Explosive Devices","volume":"53","author":"Chantasen","year":"2018","journal-title":"Radio Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Boonpoonga, A. (December, January 30). Ground Penetrating Radar (GPR) for Counter Improvised-Explosive Devices in Thailand. Proceedings of the 2015 IEEE Conference on Antenna Measurements & Applications (CAMA), Chiang Mai, Thailand.","DOI":"10.1109\/CAMA.2015.7428148"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.1109\/JSTARS.2014.2321276","article-title":"A Comparative Study of GPR Reconstruction Approaches for Landmine Detection","volume":"7","author":"Catapano","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Marsh, L.A., van Verre, W., Davidson, J.L., Gao, X., Podd, F.J.W., Daniels, D.J., and Peyton, A.J. (2019). Combining Electromagnetic Spectroscopy and Ground-Penetrating Radar for the Detection of Anti-Personnel Landmines. Sensors, 19.","DOI":"10.3390\/s19153390"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3464","DOI":"10.1109\/TGRS.2013.2273082","article-title":"Influence of Heterogeneous Soils and Clutter on the Performance of Ground-Penetrating Radar for Landmine Detection","volume":"52","author":"Takahashi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"\u0160ipo\u0161, D., and Gleich, D. (2021). SFCW Radar with an Integrated Static Target Echo Cancellation System. Sensors, 21.","DOI":"10.3390\/s21175829"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"\u0160ipo\u0161, D., and Gleich, D. (2020). A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection. Sensors, 20.","DOI":"10.3390\/s20082234"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4626","DOI":"10.1080\/01431161.2020.1723177","article-title":"Permittivity estimation of a shallow-layered medium using high-resolution ground-penetrating radar","volume":"41","author":"Bannawat","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105610","DOI":"10.1109\/ACCESS.2020.2999894","article-title":"Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar","volume":"8","author":"Chantasen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1109\/TGRS.2010.2084093","article-title":"Exploiting Ground-Penetrating Radar Phenomenology in a Context-Dependent Framework for Landmine Detection and Discrimination","volume":"49","author":"Ratto","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1109\/TGRS.2004.837333","article-title":"Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening","volume":"42","author":"Gader","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3739","DOI":"10.1109\/TGRS.2008.2002028","article-title":"Particle Filtering Based Approach for Landmine Detection Using Ground Penetrating Radar","volume":"46","author":"Ng","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TGRS.2004.839431","article-title":"Application of feature extraction methods for landmine detection using the Wichmann\/Niitek ground-penetrating radar","volume":"43","author":"Zhu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/JSEN.2010.2091500","article-title":"A Least Squares Approach to Buried Object Detection Using Ground Penetrating Radar","volume":"11","author":"Yoldemir","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/LGRS.2011.2177514","article-title":"Subsurface Imaging Using a Handheld GPR MD System","volume":"9","author":"Feng","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/JSTARS.2011.2164573","article-title":"Hand-Held GPR Imaging Using Migration for Irregular Data","volume":"4","author":"Feng","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2020.3036671","article-title":"Ground Surface Reflection Compensation for Hand-Held GPR","volume":"19","author":"Kondo","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1109\/TGRS.2009.2012701","article-title":"Automatic Analysis of GPR Images: A Pattern-Recognition Approach","volume":"47","author":"Pasolli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39009","DOI":"10.1109\/ACCESS.2021.3064205","article-title":"A Gans-Based Deep Learning Framework for Automatic Subsurface Object Recognition From Ground Penetrating Radar Data","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3398","DOI":"10.1109\/TGRS.2018.2799586","article-title":"An Automatic GPR B-Scan Image Interpreting Model","volume":"56","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7022","DOI":"10.1109\/TGRS.2020.2978763","article-title":"Application of Convolutional and Recurrent Neural Networks for Buried Threat Detection Using Ground Penetrating Radar Data","volume":"58","author":"Moalla","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TGRS.2015.2462727","article-title":"Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium","volume":"54","author":"Mertens","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1109\/LRA.2021.3062599","article-title":"GPR-RCNN: An Algorithm of Subsurface Defect Detection for Airport Runway Based on GPR","volume":"6","author":"Li","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4446","DOI":"10.1109\/JSTARS.2019.2953505","article-title":"Triplanar Imaging of 3-D GPR Data for Deep-Learning-Based Underground Object Detection","volume":"12","author":"Kim","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1002\/(SICI)1098-1098(1998)9:1<51::AID-IMA7>3.0.CO;2-Q","article-title":"Advanced image-processing technique for real-time interpretation of ground-penetrating radar images","volume":"9","author":"Capineri","year":"1998","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/36.752207","article-title":"Detection of shallowly buried objects using impulse radar","volume":"37","author":"Brunzell","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/36.843039","article-title":"A fuzzy shell clustering approach to recognize hyperbolic signatures in subsurface radar images","volume":"38","author":"Delbo","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/36.842008","article-title":"Neural detection of pipe signatures in ground penetrating radar images","volume":"38","author":"Gamba","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0926-9851(99)00055-5","article-title":"Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition","volume":"43","author":"Huang","year":"2000","journal-title":"J. Appl. Geophys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, M., Shi, P., Xu, X., Xu, X., Liu, W., and Yang, H. (2022). Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms. Sensors, 22.","DOI":"10.3390\/s22187089"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zu, L., Zhao, Y., Liu, J., Su, F., Zhang, Y., and Liu, P. (2021). Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach. Sensors, 21.","DOI":"10.3390\/s21237842"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chifor, R., Hotoleanu, M., Marita, T., Arsenescu, T., Socaciu, M.A., Badea, I.C., and Chifor, I. (2022). Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality. Sensors, 22.","DOI":"10.3390\/s22197101"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"An, Q., Wu, S., Shi, R., Wang, H., Yu, J., and Li, Z. (2022). Intelligent Detection of Hazardous Goods Vehicles and Determination of Risk Grade Based on Deep Learning. Sensors, 22.","DOI":"10.3390\/s22197123"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15708","DOI":"10.1109\/ACCESS.2021.3052851","article-title":"FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Na\u00efve Bayes for Enhanced Eye Detection","volume":"9","author":"Nsaif","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"182218","DOI":"10.1109\/ACCESS.2020.3029050","article-title":"Radiation Enhancement of an Ultrawideband Unidirectional Folded Bowtie Antenna for GPR Applications","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TAP.2017.2786295","article-title":"Wideband Reflector-Backed Folded Bowtie Antenna for Ground Penetrating Radar","volume":"66","author":"Serhir","year":"2018","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/LGRS.2019.2942007","article-title":"Low-Profile, Low-Frequency, UWB Antenna for Imaging of Deeply Buried Targets","volume":"17","author":"Yektakhah","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"44244","DOI":"10.1109\/ACCESS.2018.2864618","article-title":"A Compact Antipodal Tapered Slot Antenna With Artificial Material Lens and Reflector for GPR Applications","volume":"6","author":"Guo","year":"2018","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TAP.2006.880729","article-title":"Double-Sided Exponentially Tapered GPR Antenna and Its Transmission Line Feed Structure","volume":"54","author":"Chen","year":"2006","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1080\/00450618.2021.1921270","article-title":"Determination of historical graves by ground penetrating radar method: Sakarya Field Battle (August 23\u2013September 13, 1921, Turkey)","volume":"54","author":"Kosaroglu","year":"2021","journal-title":"Aust. J. Forensic Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Daniels, D.J. (2004). Ground Penetrating Radar, The Institution of Electrical Engineers. [2nd ed.].","DOI":"10.1049\/PBRA015E"},{"key":"ref_43","first-page":"10","article-title":"Underground object characterization based on neural networks for ground penetrating radar data","volume":"9804","author":"Zhang","year":"2016","journal-title":"Proc. SPIE"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chomdee, P., Boonpoonga, A., and Prayote, A. (2014, January 1\u20133). Fast and Efficient Detection of Buried Object for GPR Image. Proceedings of the 20th Asia-Pacific Conference on Communication (APCC2014), Pattaya, Thailand.","DOI":"10.1109\/APCC.2014.7092835"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:29Z","timestamp":1760145269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,11]]},"references-count":45,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228710"],"URL":"https:\/\/doi.org\/10.3390\/s22228710","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,11]]}}}