Analysis of Deep CNN-based Ground Penetrating Radar (GPR) Image Classification Process using Explainable AI

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dc.contributor.authorAlpdemir, Mahmut Nedim
dc.contributor.authorSezgin, Mehmet
dc.date.accessioned2023-12-21T07:51:57Z
dc.date.available2023-12-21T07:51:57Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDeep Learning techniques are increasingly being used for Ground Penetrating Radar (GPR)-based target detection and classification. However, DL models normally act as black box systems leading to difficulties regarding interpretation, re peatability and robustness of target detection and classification processes. In this paper, we employ several variants of Class Activation Map (CAM) technique to superimpose colored coded heat maps on a particular output of GPR systems, namely B-Scan images, to expose the degree of contribution of specific spatial regions to the prediction of the DL model. As such, by comparing CAM results of B-Scan images that are subjected to different pre processing stages, we aim to provide insight into the relationship between those pre-processing steps and the corresponding detec tion performance of the model. Thus, our approach facilitates more informed selection of signal processing techniques for the early phases of the GPR processing pipeline (i.e., earlier than the DL model ) on one hand; and could lead to more accurate design of the higher level decision processes, which, in turn, would increase the overall operational effectiveness of the GPR systems.en_US
dc.identifier.doi10.1109/ASYU58738.2023.10296758en_US
dc.identifier.scopus2-s2.0-85178279092en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7055
dc.identifier.urihttps://doi.org/10.1109/ASYU58738.2023.10296758
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectGround penetrating radar, explainable AI, class activation map, convolutional neural networks, target classificationen_US
dc.titleAnalysis of Deep CNN-based Ground Penetrating Radar (GPR) Image Classification Process using Explainable AIen_US
dc.typeConference Objecten_US

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