# Metal Parts Defect Detection Dataset (MPDD)
MPDD is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 1000 images with pixel-precise defect annotation masks. The dataset is divided into the training subset with anomaly-free samples and the validation subset that contains both normal and anomalous samples. The dataset can be downloaded at the following link.
Download link
## Paper
For more information about the dataset, see our paper at the following link
## Citing
If you use the dataset in this repository, please cite
```
@INPROCEEDINGS{9631567,
author={Jezek, Stepan and Jonak, Martin and Burget, Radim and Dvorak, Pavel and Skotak, Milos},
booktitle={2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)},
title={Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions},
year={2021},
volume={},
number={},
pages={66-71},
doi={10.1109/ICUMT54235.2021.9631567}
}
```
## Contact to authors
For more information, please contact us by email.
Stepan Jezek: Stepan.Jezek1@vut.cz
Radim Burget: burgetrm@vut.cz
## Acknowledgments
This work was supported by project "Defectoscopy of painted parts using automatic adaptation of neural networks", FW03010273, Technology Agency of the Czech Republic
Brno University of Technology