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This paper presents an AI-based system for improving cycle time measurement in industrial environments, leveraging YOLOv8 for object detection and ByteTrack for tracking. Our non-invasive approach analyzes video from an Azure Kinect camera to calculate cycle times by detecting objects and monitoring their state changes. Tested at the University of Applied Sciences Kempten’s demo plant, the system showcased high accuracy against ground truth data, highlighting its potential to enhance production line monitoring and efficiency significantly. This work contributes to industrial automation by offering a real-time, accurate method for cycle time analysis, promising substantial advancements in manufacturing process optimization.

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Cycle Time Measurement Using AI-Based Object Detection and Tracking in Industrial Processes

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Tim Staudenrausch,
  • Bernd Lüdemann-Ravit

摘要

This paper presents an AI-based system for improving cycle time measurement in industrial environments, leveraging YOLOv8 for object detection and ByteTrack for tracking. Our non-invasive approach analyzes video from an Azure Kinect camera to calculate cycle times by detecting objects and monitoring their state changes. Tested at the University of Applied Sciences Kempten’s demo plant, the system showcased high accuracy against ground truth data, highlighting its potential to enhance production line monitoring and efficiency significantly. This work contributes to industrial automation by offering a real-time, accurate method for cycle time analysis, promising substantial advancements in manufacturing process optimization.