Visual quality inspection using computer vision and deep learning

Joanna Rosak-Szyrocka and Radoslaw Wolniak

Chapter 7 in Quality 4.0 and Artificial Intelligence, 2026, pp 118-133 from Edward Elgar Publishing

Abstract: This chapter investigates how computer vision and deep learning intersect as revolutionary instruments for visual quality inspection in production. It follows the journey from traditional human-based methods towards automated, scalable solutions through convolutional neural networks, transfer learning and vision transformers to facilitate precise defect detection even amidst heterogeneous and dynamic production environments. Fingerprints are laid on edge AI processors, real-time vision systems, and integration of MES/SCADA platforms in an effort to ensure latency-free monitoring, full traceability and predictive quality control. Based on food, electronics, pharma, textiles and automotive sector case studies, the chapter foresees both the potential and limitations of such technology with challenges like dataset imbalance, model explainability, and scalability across sectors. Finally, it makes visual AI inspection a cornerstone of Industry 4.0 and a driver for human-centric Industry 5.0 environments where man–machine collaboration ensures efficiency, compliance and sustainability in manufacturing.

Keywords: Visual quality inspection; Computer vision; Deep learning; Human-AI collaboration; Industry 4.0; Industry 5.0; Transfer learning; Industrial applications (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035397068
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