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Abstract <p>The “Morris water maze” presents a crucial challenge for tracking the moving small laboratory animal. Previously, we had solved the problem of obtaining the trajectory by analyzing video data using computer vision methods. To develop a more universal algorithm, this paper presents a trajectory construction algorithm based on the YOLO11 deep learning model, which is used for frame-by-frame object detection. The developed method shows promising results in overcoming the shortcomings of the previous approach.</p>

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A Deep Learning Algorithm for Tracking Laboratory Animals in the “Morris Water Maze”

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • T. Zh. Bezhanyan,
  • O. I. Streltsova,
  • S. Shadmehri,
  • A. V. Stadnik,
  • D. V. Podgainy,
  • M. Yu. Bondarev,
  • M. I. Zuev,
  • Yu. S. Severiukhin,
  • I. A. Kolesnikova

摘要

Abstract

The “Morris water maze” presents a crucial challenge for tracking the moving small laboratory animal. Previously, we had solved the problem of obtaining the trajectory by analyzing video data using computer vision methods. To develop a more universal algorithm, this paper presents a trajectory construction algorithm based on the YOLO11 deep learning model, which is used for frame-by-frame object detection. The developed method shows promising results in overcoming the shortcomings of the previous approach.