Corrosion Image Data Set for Automating Scientific Assessment of Materials

MIT License © [2021] [Worcester Polytechnic Institute] This corrosion image data set has been introduced by the paper, “Corrosion Image Data Set for Automating Scientific Assessment of Materials” published at the British Machine Vision Conference BMVC 2021. Please follow the instructions on our website for downloading, use and proper citation of the data set. Additional details on the data included on the download is found in the downloaded README file.Introduced 2021-11-22

The study of material corrosion is an important research area, with corrosion degradation of metallic structures causing expenses up to 4% of the global domestic product annually along with major safety risks worldwide. Unfortunately, large-scale and timely scientific discovery of materials has been hindered by the lack of standardized corrosion experimental data in the public domain for developing machine learning models. Obtaining such data is challenging due to the expert knowledge and time required to conduct these scientific experiments and assess corrosion levels. We curate a novel dataset consisting of 600 images annotated with expert corrosion ratings obtained over 10 years of laboratory corrosion testing by material scientists. Based on this data set, we find that non-experts even when rigorously trained with domain guidelines to rate corrosion fail to match expert ratings. Challenges include limited data, image artifacts, and millimeter-precision corrosion. This motivates us to explore the viability of deep learning approaches to tackle this benchmark classification task. We study (i) convolutional neural networks powered with rich domain-specific image augmentation techniques tuned to our data, and (ii) a recent self-supervised representation learning approach either pretrained on ImageNet or trained on our data. We demonstrate that pretrained ResNet-18 and HR-Net models with tuned augmentations can reach up to 0.83 accuracy. With this corrosion data set, we open the door for the design of more advanced deep learning models to support this real-world task, while driving innovative new research to bridge computer vision and material innovation.

[Disclaimer]

By downloading this code and/or using this data, you agree to abide by all of the rules and regulations.

  1. Researcher shall use the dataset only for non-commercial research and educational purposes.
  2. The authors with Worcester Polytechnic Institute and US Army Research Lab make no representations or warranties regarding the dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
  3. Researcher accepts full responsibility for his or her use of the dataset and shall defend and indemnify the authors, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the dataset, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the dataset.
  4. Researcher may provide research associates and colleagues with access to the dataset provided that they first agree to be bound by these terms and conditions.
  5. The authors reserve the right to terminate Researcher's access to or usage of the dataset at any time.
  6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
  7. The law of the State of Massachusetts shall apply to all disputes under this agreement.

[Citation]

Anyone that uses this data set must cite and acknowledge our BMVC data set paper:

@InProceedings{yin2021BMVC, author = {Yin, Biao and Josselyn, Nicholas and Considine, Thomas and Kelley, John and Rinderspacher, Berend and Jensen, Robert and Snyder, James and Zhang, Ziming and Rundensteiner, Elke}, title = {Corrosion Image Data Set for Automating Scientific Assessment of Materials}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2021}}