Alpdemir, Mahmut NedimSezgin, Mehmet2023-12-212023-12-212023https://hdl.handle.net/11467/7055https://doi.org/10.1109/ASYU58738.2023.10296758Deep 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.eninfo:eu-repo/semantics/embargoedAccessGround penetrating radar, explainable AI, class activation map, convolutional neural networks, target classificationConference ObjectN/A2-s2.0-8517827909210.1109/ASYU58738.2023.10296758