{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:23:38Z","timestamp":1759134218302,"version":"3.28.0"},"reference-count":16,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Electron."],"published-print":{"date-parts":[[2024,11,1]]},"DOI":"10.1587\/transele.2023ess0005","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T22:13:56Z","timestamp":1714083236000},"page":"497-500","source":"Crossref","is-referenced-by-count":1,"title":["A Simple Augmentation Method Using Cutout for Ground Penetrating Radar Image in Deep Learning"],"prefix":"10.1587","volume":"E107.C","author":[{"given":"Jun","family":"SONODA","sequence":"first","affiliation":[{"name":"National Institute of Technology, Sendai College"}]},{"given":"Kazusa","family":"NAKAMICHI","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Sendai College"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] D.J. Daniels, Surface-Penetrating Radar, Inst of Engineering & Technology, London, 1996.","DOI":"10.1049\/ecej:19960402"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] K. Dinh, N. Gucunski, and T.H. Duong, \u201cAn algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks,\u201d Automation in Construction, vol.89, pp.292-298, 2018. 10.1016\/j.autcon.2018.02.017","DOI":"10.1016\/j.autcon.2018.02.017"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] T. Yamaguchi, T. Mizutani, and T. Nagayama, \u201cMapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images,\u201d IEEE Trans. Geosci. and Remote Sens., vol.59, no.8, pp.6525-6536, Aug. 2021. 10.1109\/tgrs.2020.3030079","DOI":"10.1109\/TGRS.2020.3030079"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] J. Wang, H. Liu, P. Jiang, Z. Wang, Q. Sui, and F. Zhang, \u201cGPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines,\u201d IEEE Trans. Geosci. and Remote Sens., vol.60, pp.1-20, 2022. 10.1109\/tgrs.2021.3111445","DOI":"10.1109\/TGRS.2021.3111445"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] B.P.A. Rohman, M. Nishimoto, and K. Ogata, \u201cReconstruction of Missing Ground-Penetrating Radar Traces Using Simplified U-Net,\u201d IEEE Geosci. and Remote Sens. Lett., vol.19, pp.1-5, 2022. 10.1109\/lgrs.2021.3072028","DOI":"10.1109\/LGRS.2021.3072028"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J. Wang, K. Chen, H. Liu, J. Zhang, W. Kang, S. Li, P. Jiang, Q. Sui, and Z. Wang, \u201cDeep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images,\u201d IEEE Access, vol.9, pp.87207-87218, 2021. 10.1109\/access.2021.3088630","DOI":"10.1109\/ACCESS.2021.3088630"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] Q. Dai, Y.H. Lee, H.-H. Sun, G. Ow, M.L.M. Yusof, and A.C. Yucel, \u201c3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion,\u201d IEEE Trans. Geosci. and Remote Sens., vol.61, pp.1-16, 2023. 10.1109\/tgrs.2023.3275306","DOI":"10.1109\/TGRS.2023.3275306"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Hu, H. Fang, N. Wang, H. Liu, J. Lei, D. Ma, and J. Dong, \u201cA Study of Automatic Recognition and Localization of Pipeline for Ground Penetrating Radar Based on Deep Learning,\u201d IEEE Geosci. and Remote Sens. Lett., vol.19, pp.1-5, 2022. 10.1109\/lgrs.2022.3198439","DOI":"10.1109\/LGRS.2022.3198439"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T. Yamaguchi, T. Mizutani, K. Meguro, and T. Hirano, \u201cDetecting Subsurface Voids From GPR Images by 3-D Convolutional Neural Network Using 2-D Finite Difference Time Domain Method,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.15, pp.3061-3073, 2022. 10.1109\/jstars.2022.3165660","DOI":"10.1109\/JSTARS.2022.3165660"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] J. Sonoda and T. Kimoto, \u201cObject identification form GPR images by deep learning,\u201d Proc. Asia-Pac. Microw. Conf., pp.1298-1300, Kyoto, Japan, Nov. 2018. 10.23919\/apmc.2018.8617556","DOI":"10.23919\/APMC.2018.8617556"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] S. Li, X. Cui, L. Guo, L. Zhang, X. Chen, and X. Cao, \u201cEnhanced Automatic Root Recognition and Localization in GPR Images Through a YOLOv4-Based Deep Learning Approach,\u201d IEEE Trans. Geosci. and Remote Sens., vol.60, 2022. 10.1109\/tgrs.2022.3181202","DOI":"10.1109\/TGRS.2022.3181202"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] A. Fazeel, J. Rottmayer, R. Mehta, and N. Bajcinca, \u201cGPR-GANs: Generation of Synthetic Ground Penetrating Radargrams Using Generative Adversarial Networks,\u201d 2021 11th International Workshop on Advanced Ground Penetrating Radar, Valletta, Malta, Dec. 2021. 10.1109\/iwagpr50767.2021.9843171","DOI":"10.1109\/IWAGPR50767.2021.9843171"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] G. Chen, X. Bai, G. Wang, L. Wang, X. Luo, M. Ji, P. Feng, and Y. Zhang, \u201cSubsurface Voids Detection from Limited Ground Penetrating Radar Data Using Generative Adversarial Network and YOLOV5,\u201d 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, July 2021. 10.1109\/igarss47720.2021.9554954","DOI":"10.1109\/IGARSS47720.2021.9554954"},{"key":"14","unstructured":"[14] T. DeVries and G.W. Taylor, \u201cImproved Regularization of Convolutional Neural Networks with Cutout,\u201d arXiv preprint arXiv: 1708. 04552, 2017."},{"key":"15","unstructured":"[15] Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, \u201cRandom Erasing Data Augmentation,\u201d arXiv preprint arXiv:1708.04896, 2017."},{"key":"16","unstructured":"[16] J. Redmon and A. Farhadi, \u201cYOLOv3: An Incremental Improvement,\u201d arXiv preprint arXiv:1804.02767, 2018."}],"container-title":["IEICE Transactions on Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transele\/E107.C\/11\/E107.C_2023ESS0005\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T03:23:45Z","timestamp":1730517825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transele\/E107.C\/11\/E107.C_2023ESS0005\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"references-count":16,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transele.2023ess0005","relation":{},"ISSN":["0916-8524","1745-1353"],"issn-type":[{"type":"print","value":"0916-8524"},{"type":"electronic","value":"1745-1353"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"article-number":"2023ESS0005"}}