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Computer Science > Machine Learning

arXiv:2109.02469 (cs)
[Submitted on 6 Sep 2021]

Title:Active Learning for Automated Visual Inspection of Manufactured Products

Authors:Elena Trajkova, Jože M. Rožanec, Paulien Dam, Blaž Fortuna, Dunja Mladenić
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Abstract: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.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2109.02469 [cs.LG]
  (or arXiv:2109.02469v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.02469

Submission history

From: Jože Rožanec [view email]
[v1] Mon, 6 Sep 2021 13:44:25 UTC (771 KB)
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