Rebar Detection using YOLO Models

Select a model, upload a GPR image, set processing parameters, and the model will predict rebar locations. Note: This is a prototype implementation running on a vCPU. The image slicing is currently based on pixels. For practical applications, slicing should be performed by distance (meters) in the horizontal direction.
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Example Images
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📜 Paper Information

This Space is based on the research presented in our paper for IWAGPR25:

@inproceedings{elseicy2025rebar,
    title     = {Preliminary Study on Automating Rebar Detection in Reinforced Concrete Structures Using YOLOv11 and GPR Data},
    author    = {Elseicy, Ahmed and Solla, Mercedes and Novo, Alexandre},
    year      = {2025},
    month     = {July},
    booktitle = {2025 13th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)},
    publisher = {IEEE},
    pages     = {323--328},
    isbn      = {979-8-3315-2335-0},
    issn      = {2687-7899}
}

💾 Dataset Reference

The full models and the dataset used in the project are published in Zenodo DOI: 10.5281/zenodo.16638791.

💰 Funding Acknowledgement

This research and development were made possible through the OVERSIGHT project (PID2022-138526OB-I00) funded by MICIU/AEI/10.13039/501100011033/FEDER, UE. Grant PREP2022-000030 for the training of predoctoral researchers funded by MICIU/ AEI/10.13039/501100011033 and by FSE+. M. Solla acknowledges the Grant RYC2019–026604–I funded by MICIU/ AEI/10.13039/501100011033 and by “ESF Investing in your future”.