Datasetpublic

tsrobcvai/ROI-1555_Rebar_Detection_and_Instance_Segmentation_Dataset

ROI-1555: Rebar Detection and Instance Segmentation Dataset ROI-1555 for rebar object detection and instance segmentation contains 1555 rebar images and their fine-labeled bounding boxes and pixel-wise masks. Diverse rebar specifications, layouts, application scenarios, and environmental conditions. Usage Here is an example to convert the annotations to MSCOCO 2017 format python cp -r 1260/img_label tools/data_annotated/train2017 cd tools python… See the full description on the dataset page: https://huggingface.co/datasets/tsrobcvai/ROI-1555_Rebar_Detection_and_Instance_Segmentation_Dataset.

sourceHugging Faceupdated 1y agoView on Hugging Face

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Dataset Card

<h2 align="center">ROI-1555: Rebar Detection and Instance Segmentation Dataset</a></h3>

—ROI-1555 for rebar object detection and instance segmentation contains 1555 rebar images and their fine-labeled bounding boxes and pixel-wise masks.
—Diverse rebar specifications, layouts, application scenarios, and environmental conditions.

Usage

—Here is an example to convert the annotations to MSCOCO 2017 format

python 
cp -r 1260/img_label tools/data_annotated/train2017
cd tools
python labelme2coco_instance.py
#Annotation instances_train2017.json(MSCOCO 2017 format) will be generated in tools/annotations

Paper

<h2 align="center"><a href="https://www.sciencedirect.com/science/article/pii/S147403462500117X">Deep Learning-based Rebar Detection and Instance Segmentation in Images</a></h3> <p align="center"> <a href="https://www.shao-lab.com/Team-c7cad4a2a33a4d7686e0b6e8a524b816">Tao Sun</a>, Qipei Fan</a>, <a href="https://www.shao-lab.com/Learn-more-4469738c5183485884effe68c04e692d">Yi Shao*</a>

<br> Advanced Engineering Informatics </p>

If you use the dataset in your work, please cite our paper:

@article{sun2025deep,
  title={Deep learning-based rebar detection and instance segmentation in images},
  author={Sun, Tao and Fan, Qipei and Shao, Yi},
  journal={Advanced Engineering Informatics},
  volume={65},
  pages={103224},
  year={2025},
  publisher={Elsevier}
}

Highlights

—Mask2Former trained on our dataset shows good generalization ability in unseen data <div align="center"> <img src="doc/img2.png" width="900"></img> </div>

—Benchmark test shows the performance and limitations of the popular networks <div align="center"> <img src="doc/img3.png" width="900"></img> </div>

—Six data augmentation strategies were introduced and tested to improve the SOTA method, which can guide the selection of suitable data augmentation strategies for rebar perception.

Methods	Epochs	mAP	mAP50	mAP75	mAPstraight	mAPhoop
Without additional data augmentation	178	68.6	93.6	79.9	76.0	61.1
With Random Vertical-Flip	178	60.1	91.7	65.8	70.1	50.1
With Random Rotation	178	66.9	94.4	77.6	75.1	58.8
With Mosaic	178	54.1	85.6	58.7	61.4	46.8
With MixUp	356	64.0	94.1	71.8	72.6	55.4
With Cutout	356	70.2	94.2	81.9	77.5	62.8
With Simple Copy-Paste	718	71.4	93.9	84.7	78.5	64.3

</p>

Contact

If you have any questions on the dataset, please email tao.sun@mail.mcgill.ca.