# Active Learning for Automated Visual Inspection of Manufactured Products

arXiv: https://arxiv.org/abs/2109.02469 · HTML: https://mlsanity.com/paper/2109.02469/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [Active learning using weakly supervised signals for quality inspection](https://mlsanity.com/paper/2104.02973/) (2021)

## Built on by (most cited first, 3 of 3)

- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [Using Active Learning Methods to Strategically Select Essays for Automated Scoring](https://mlsanity.com/paper/2301.00628/) (2023)

## Abstract (arXiv)

Quality control is a key activity performed by manufacturing enterprises to ensure products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. In this research, we compare three active learning approaches and five machine learning algorithms applied to visual defect inspection with real-world data provided by Philips Consumer Lifestyle BV. Our results show that active learning reduces the data labeling effort without detriment to the models' performance.