mlsanityData to 7 Oct 2026

Industrial Machine Tool Component Surface Defect Dataset

arXiv 2103.13003 ↗First posted 24 Mar 2021cs.CV eess.IVcited by 5

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Abstract (arXiv)

Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data often not available in large quantities in technical domains. The manual inspection of machine tool components and the manual end-of-line check of products are labor-intensive tasks in industrial applications that companies often want to automate. To automate classification processes and develop reliable and robust machine learning-based classification and wear prognostics models, one needs real-world datasets to train and test the models. The dataset is available under https://doi.org/10.5445/IR/1000129520.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 24 Mar 2021.

Builds on

6 of 30 references.

References · 6

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017Mask R-CNNcited by 7,259
  4. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  5. 2019SOLO: Segmenting Objects by Locationscited by 289
  6. 2020Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applicationscited by 63

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.

5 papers.