Industrial Machine Tool Component Surface Defect Dataset
Not on the map. Why?
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
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Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.
5 papers.
- 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 49
- 2023Defect detection using weakly supervised learningcited by 2
- 2024SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspectioncited by 2
- 2025FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detectioncited by 1
- 2026From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metalcited by 1
- 2026From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal5 Feb 2026
- 2025FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection1 Dec 2025
- 2024SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection18 Oct 2024
- 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation17 Jul 2024
- 2023Defect detection using weakly supervised learning27 Mar 2023