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Supervised and Self-Supervised Learning for Assembly Line Action Recognition.

Christopher Indris1, Fady Ibrahim1, Hatem Ibrahem1

  • 1Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

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|January 24, 2025
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Summary
This summary is machine-generated.

This study introduces a real-time system for monitoring assembly line workers using semi-supervised temporal action recognition. The I3D model achieved 85% accuracy, demonstrating potential for improved manufacturing safety and efficiency.

Keywords:
action recognitionassembly line monitoringcomputer visionreal-time feature extractionsemi-supervised learningsupervised learningtemporal action localization

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Engineering

Background:

  • Manufacturing safety and efficiency are paramount.
  • Human supervision limitations on dynamic assembly lines necessitate automated monitoring.
  • Real-time worker action recognition is crucial for operational oversight.

Purpose of the Study:

  • To develop and benchmark a real-time, semi-supervised temporal action recognition system for assembly line monitoring.
  • To compare the performance of various feature extractors and localization models.
  • To explore self-supervised learning approaches for action recognition with limited labeled data.

Main Methods:

  • A new assembly line dataset was created for benchmarking.
  • The I3D model was evaluated for fully-supervised action recognition.
  • A modified SPOT model was adapted for semi-supervised learning using a subset of labeled data.

Main Results:

  • The I3D model achieved a high average mAP@IoU=0.1:0.7 of 85% without optical flow or fine-tuning.
  • The semi-supervised SPOT model reached 65% mAP@IoU=0.1:0.7 with only 10% labeled data.
  • Significant performance was observed for both supervised and semi-supervised methods on the new dataset.

Conclusions:

  • The developed system shows strong potential for scalable real-time worker action recognition.
  • Findings support the feasibility of semi-supervised learning for reducing labeling costs in industrial settings.
  • The research paves the way for enhanced labor efficiency and safety compliance in manufacturing.