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Related Experiment Video

Updated: Jan 13, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Automatic Visual Inspection for Industrial Application.

António Gouveia Ribeiro1,2, Luís Vilaça1,2, Carlos Costa3

  • 1INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal.

Journal of Imaging
|October 28, 2025
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Summary

This study introduces an automated visual inspection system for pharmaceutical bottles, utilizing incremental learning to adapt to new defects. The system enhances quality control by improving defect detection accuracy and reliability in industrial settings.

Keywords:
defect detectionsincremental learningindustrial environmentmachine learningmulti-viewquality control

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Area of Science:

  • Industrial Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Manual quality control in pharmaceuticals is error-prone and inefficient.
  • Automated solutions are needed for evolving industrial environments and unpredictable defects.

Purpose of the Study:

  • To develop an automated visual defect detection system for pharmaceutical bottles.
  • To enable adaptation to new defect types using incremental learning.

Main Methods:

  • Integration of various computer vision and machine learning techniques.
  • Implementation of incremental learning for model adaptation.
  • Multi-view inspection setup for comprehensive image capture.

Main Results:

  • High defect detection rates achieved in real-world industrial conditions.
  • Demonstrated robustness and accuracy in identifying pharmaceutical bottle defects.
  • Successful adaptation to new and rare defect types without full retraining.

Conclusions:

  • The proposed automated system offers a reliable and adaptable solution for pharmaceutical quality control.
  • Incremental learning is key to handling evolving defect landscapes.
  • The system shows potential for broader applications in manufacturing defect detection.