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<p>The survival of the red panda, an endangered arboreal mammal, is challenged by two main factors: habitat loss and health risks that contribute to high morbidity and mortality. Abnormal behaviors, such as reduced social and locomotor behaviors and sleep deprivation, are often signals of potential health problems. Non-invasive behavioral monitoring using computer vision can provide valuable insights to advance health research and welfare practices. This study presents a dataset of 3142 images of red panda behavior, collected using a motion-activated camera and web crawler technology at Bifengxia Wildlife World. This study proposes an improved lightweight and efficient YOLOv8 model for behavior recognition. The model incorporates adaptive histogram equalization and the GMBottleNeck module, which enhance detail accentuation and reduce parameters. The training process was enhanced through the integration of the SimAM attention mechanism and feature fusion learning. The aforementioned enhancements led to the YOLOv8s-Red Panda model attaining a 90.6% accuracy rate, representing a 1.4% improvement and a 1/3 reduction in model size in comparison to the data-enhanced baseline model (YOLOv8s-DE). The model exhibits exemplary performance in the recognition of red panda behavior, with the potential to significantly advance healthcare and optimize animal welfare.</p>

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A computer vision solution for behavioral recognition in red pandas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月17日

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  • Pu Luo,
  • Yupeng Niu,
  • Duoxun Tang,
  • Wenyuan Huang,
  • Xuefei Luo,
  • Jiong Mu

摘要

The survival of the red panda, an endangered arboreal mammal, is challenged by two main factors: habitat loss and health risks that contribute to high morbidity and mortality. Abnormal behaviors, such as reduced social and locomotor behaviors and sleep deprivation, are often signals of potential health problems. Non-invasive behavioral monitoring using computer vision can provide valuable insights to advance health research and welfare practices. This study presents a dataset of 3142 images of red panda behavior, collected using a motion-activated camera and web crawler technology at Bifengxia Wildlife World. This study proposes an improved lightweight and efficient YOLOv8 model for behavior recognition. The model incorporates adaptive histogram equalization and the GMBottleNeck module, which enhance detail accentuation and reduce parameters. The training process was enhanced through the integration of the SimAM attention mechanism and feature fusion learning. The aforementioned enhancements led to the YOLOv8s-Red Panda model attaining a 90.6% accuracy rate, representing a 1.4% improvement and a 1/3 reduction in model size in comparison to the data-enhanced baseline model (YOLOv8s-DE). The model exhibits exemplary performance in the recognition of red panda behavior, with the potential to significantly advance healthcare and optimize animal welfare.