Classification of Buried Objects Using Deep Learning on GPR Data
| dc.contributor.author | Sezgin, Mehmet | |
| dc.contributor.author | Alpdemir, Mahmut Nedim | |
| dc.date.accessioned | 2023-11-10T09:18:34Z | |
| dc.date.available | 2023-11-10T09:18:34Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | In this study, we present the results of two-class identification of buried objects using convolutional neural networks on real GPR dataset with 1080 images. The dataset includes GPR images of clutter objects and surrogate mines. While clutter class consist of stones, cans, bottles, nails and similar objects, the surrogate mine class consists of metallic and non-metallic anti-personnel and anti-tank surrogate mines. We obtained nearly 100% classification results for two-class classification. | en_US |
| dc.identifier.doi | 10.1109/IC_ASET58101.2023.10150717 | en_US |
| dc.identifier.scopus | 2-s2.0-85164254166 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11467/6986 | |
| dc.identifier.uri | https://doi.org/10.1109/IC_ASET58101.2023.10150717 | |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | Ground penetrating radar, identification, convolutional neural network | en_US |
| dc.title | Classification of Buried Objects Using Deep Learning on GPR Data | en_US |
| dc.type | Conference Object | en_US |
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