开发一种方法,通过结合三维声波系统和深度神经网络来估计阿萨里的分布
Tokimu Kadoi1, Katsunori Mizuno2, Shoichi Ishida1
1Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
这项研究引入了使用高频超声波和3D卷积神经网络 (3D-CNN) 进行海洋生物非破坏性监测的新系统. 该技术准确地估计了盆地资源的分布,有助于可持续管理.
科学领域:
- 海洋生物学 海洋生物学
- 声学技术 声学技术
- 机器学习是机器学习.
背景情况:
- 可持续的海洋资源管理需要非接触式,非破坏性的监测方法.
- 水下成像和声学数据分析的进步改善了数据采集.
- 对于盆地资源的大型3D声学数据集的手动分析具有挑战性,需要自动化解决方案.
研究的目的:
- 开发一个自动化系统,以非破坏性的方式估计盆地资源分布.
- 将高精度息地数据采集与3D卷积神经网络 (3D-CNN) 预测模型相结合.
- 评估系统在海底下测量海洋资源的有效性.
主要方法:
- 开发了一个系统,将高频超声波数据采集与3D-CNN模型集成在一起.
- 利用高精度息地数据进行预测.
- 应用该系统来估计日本哈马纳湖的阿萨里 (Ruditapes philippinarum) 的分布.
主要成果:
- 成功估计了阿萨里海的存在,ROC-AUC为0.9.
- 精确估计的每伏克尔的贝类数量,宏观平均ROC-AUC为0.8.8.
- 视觉化鱼分布和量化估计数量在海底下.
结论:
- 开发的系统有效量化了海底下方的海洋资源.
- 这种非破坏性监测方法支持对盆地生物种群的可持续管理.
- 超声波和3D-CNN的集成为分析大型3D声学数据集提供了有希望的解决方案.
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