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This paper addresses the critical need for accurate and rapid detection of Data Matrix (DM) codes in a tube-rack system, which is essential for efficient sample management and analysis. Our goal is to develop a real-time web system that leverages deep learning to overcome the challenges posed by rotated DM codes. We achieve this by enhancing the YOLOv5 model with rotational and attention modules, and by implementing pre- and post-processing techniques tailored for DM code detection. The system’s performance is evaluated against state-of-the-art models, demonstrating its effectiveness and potential to revolutionize laboratory automation. Our findings conclude that the integrated approach of advanced deep learning and user-centric design offers a robust solution for the accurate detection and decoding of DM codes in laboratory environments.

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Real-Time Data Matrix Code Detection for Tube-Rack System

  • 会议论文

  • 发表日期：

    2026年

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  • Qing-Bo Zhang,
  • Zi-Can Chen,
  • Dong-Xing Jing,
  • Chen-Hao Xiang,
  • Shi-Jian Liu

摘要

This paper addresses the critical need for accurate and rapid detection of Data Matrix (DM) codes in a tube-rack system, which is essential for efficient sample management and analysis. Our goal is to develop a real-time web system that leverages deep learning to overcome the challenges posed by rotated DM codes. We achieve this by enhancing the YOLOv5 model with rotational and attention modules, and by implementing pre- and post-processing techniques tailored for DM code detection. The system’s performance is evaluated against state-of-the-art models, demonstrating its effectiveness and potential to revolutionize laboratory automation. Our findings conclude that the integrated approach of advanced deep learning and user-centric design offers a robust solution for the accurate detection and decoding of DM codes in laboratory environments.