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.
For more information about the dataset, see our paper at the following link
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}
}
For more information, please contact us by email.
Stepan Jezek: Stepan.Jezek1@vut.cz Radim Burget: burgetrm@vut.cz
This work was supported by project "Defectoscopy of painted parts using automatic adaptation of neural networks", FW03010273, Technology Agency of the Czech Republic