| <h2 align="center">ROI-1555: Rebar Detection and Instance Segmentation Dataset</a></h3> | |
| <div align="center"> | |
| <img src="doc/img1.png" width="900"></img> | |
| </div> | |
| * 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. | |
| <p align="center"> | |
| | 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](mailto:tao.sun@mail.mcgill.ca). |