Yazar "Sezgin, Mehmet" seçeneğine göre listele

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    Analysis of Deep CNN-based Ground Penetrating Radar (GPR) Image Classification Process using Explainable AI
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alpdemir, Mahmut Nedim; Sezgin, Mehmet
    Deep 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.
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    Classification of Buried Objects Using Deep Learning on GPR Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sezgin, Mehmet; Alpdemir, Mahmut Nedim
    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.
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    A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection
    (Springer Science and Business Media Deutschland GmbH, 2024) Alpdemir, Mahmut Nedim; Sezgin, Mehmet
    Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.
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    A Reinforcement Learning (RL)-Based Hybrid Search Method for Hidden Object Discovery using GPR
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alpdemir, Mahmut Nedim; Sezgin, Mehmet
    Ground Penetrating Radar (GPR) systems are effective tools for discovering various types of buried objects (such as military mines, metal objects, pieces of underground infrastructures etc.). One challenge is to find an efficient search procedure that would reduce the operation time and optimize resource utilization, such as minimizing the power consumption for battery operated UAVs or fuel consumption of land vehicles. Current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper we report on a Reinforcement Learning (RL) based search method for training the guidance logic of possible platforms hosting the GPR, so that they navigate more efficiently. We illustrate the applicability of the method using a high-fidelity simulation environment and a newly implemented RL framework. Initial results suggests that combination of a coarse navigation scheme and an RL based training procedure based on GPR scan patterns, can lead to a more efficient target discovery procedure for host platforms.