---
title: Rebar Detection
emoji: 🏃
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
license: cc-by-4.0
short_description: Rebar detection models published in IWAGPR25
---
## 📜 Paper Citation

This Space is based on the research presented in our paper. If you use this work, please cite the following publication:

```bibtex
@INPROCEEDINGS{11108989,
  author={Elseicy, Ahmed and Solla, Mercedes and Novo, Alexandre},
  booktitle={2025 13th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)}, 
  title={Preliminary Study on Automating Rebar Detection in Reinforced Concrete Structures Using YOLOv11 and GPR Data}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/IWAGPR65621.2025.11108989}}

```
## 💾 Dataset Reference

The full models and the dataset used in the project are published in Zenodo.
```bibtex
@misc{elseicy_2025_16638791,
	title        = {Deep learning model for rebar detection from GPR data},
	author       = {Elseicy, Ahmed and Solla, Mercedes},
	year         = 2025,
	month        = jul,
	publisher    = {Zenodo},
	doi          = {10.5281/zenodo.16638791},
	url          = {https://doi.org/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”.