Okada Natsuo, Kubo Serina, Zhu Yanhui, Takizawa Kaito, Wakae Shion, Nakamura Amane, Igawa Sakura, Ohtomo Yoko, Minami Kenji, Miyashita Kazushi, Kawamura Youhei
Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Kita-13, Nishi-8, Sapporo, 060-8628, Japan.
Graduate School of Environmental Science, Hokkaido University, 20‑5, Benten‑cho, Hakodate, Hokkaido, 040‑0051, Japan.
Sci Rep. 2025 Aug 12;15(1):29482. doi: 10.1038/s41598-025-01138-4.
Eelgrass ecosystems provide essential habitats for species, such as the Hokkai shrimp (Pandalus latirostris), supporting biodiversity and fisheries. Understanding shrimp behavior in these environments is vital for conservation efforts, yet accurately tracking shrimp movement in controlled tank settings is challenging because of surface reflections and parallax distortions. This study primarily focuses on developing and evaluating a You Only Look Once (YOLO) tracking system for Hokkai shrimp in aquaria. We implemented preliminary measures to address reflection artifacts and parallax distortions; however, our core contribution is the robust detection performance of YOLOv8. Through controlled tank experiments, the system demonstrated high detection accuracy and captured metrics such as distance, velocity, and angle. The results showed a high detection accuracy, with success rates of 95.65% and 100% from the front and side views, respectively, ensuring reliable data on shrimp movement in aquaria. These outcomes provide a robust foundation for high-precision measurements in future ecological or aquaculture studies.
大叶藻生态系统为诸如北海道虾（Pandalus latirostris）等物种提供了重要栖息地，支持生物多样性和渔业发展。了解虾类在这些环境中的行为对于保护工作至关重要，但由于表面反射和视差畸变，在受控的水族箱环境中准确跟踪虾的移动具有挑战性。本研究主要专注于开发和评估一种用于水族箱中北海道虾的单阶段目标检测（YOLO）跟踪系统。我们采取了初步措施来解决反射伪影和视差畸变问题；然而，我们的核心贡献在于YOLOv8的强大检测性能。通过受控的水族箱实验，该系统展示了高检测精度，并获取了距离、速度和角度等指标。结果显示检测精度很高，从正面和侧面观察的成功率分别为95.65%和100%，确保了有关水族箱中虾类移动的可靠数据。这些成果为未来生态或水产养殖研究中的高精度测量提供了坚实基础。
