# Industrial Machine Tool Component Surface Defect Dataset

arXiv: https://arxiv.org/abs/2103.13003 · HTML: https://mlsanity.com/paper/2103.13003/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)

## Built on by (most cited first, 5 of 5)

- [VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation](https://mlsanity.com/paper/2407.12276/) (2024)
- [Defect detection using weakly supervised learning](https://mlsanity.com/paper/2303.15092/) (2023)
- [SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection](https://mlsanity.com/paper/2410.14844/) (2024)
- [FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection](https://mlsanity.com/paper/2512.01315/) (2025)
- [From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal](https://mlsanity.com/paper/2602.07062/) (2026)

## 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.