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 labelme2coco_instance.py
#Annotation instances_train2017.json(MSCOCO 2017 format) will be generated in tools/annotations
Paper
Deep Learning-based Rebar Detection and Instance Segmentation in Images
Tao Sun,
Qipei Fan,
Yi Shao*
Advanced Engineering Informatics
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
Benchmark test shows the performance and limitations of the popular networks
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 |
Contact
If you have any questions on the dataset, please email tao.sun@mail.mcgill.ca.