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使用YOLOv8识别叶绿素环
Guangjun Xu1,2, Zhixia Lin3, Yucheng Shi4
1School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China. gjxu_gdou@yeah.net.
Scientific reports
|April 22, 2025
概括
这项研究使用YOLOv8深度学习模型检测叶绿素环,这表明海洋浮游植物. 该模型准确地识别了这些环,为海洋生态系统和渔业提供了新的见解.
科学领域:
- 海洋生物学 海洋生物学
- 海洋学 海洋学 海洋学
- 环境科学中的人工智能
背景情况:
- 叶绿素度是海洋浮游生物和初级生产力的关键指标,对渔业至关重要.
- 深度学习模型在海洋科学中越来越多地用于数据分析.
研究的目的:
- 采用YOLOv8模型来识别大尺度海洋旋周围的叶绿素环中叶绿素度升高的情况.
- 根据的特征统计分析叶绿素环的空间分布和生成.
主要方法:
- 使用YOLOv8深度学习模型对叶绿素环的对象检测.
- 将YOLOv8的性能与Swin变压器和ResNet模型进行比较.
- 对已识别的叶绿素环进行统计分析,考虑半径和生命周期.
主要成果:
- 与其他模型相比,YOLOv8在识别叶绿素环方面表现出卓越的准确性和概括能力.
- 统计分析揭示了叶绿素环的空间分布特征.
- 在不同的半径和生命周期下量化了叶绿素环的生成.
结论:
- YOLOv8模型为分析叶绿素度数据和了解海洋生态系统提供了强大的工具.
- 这项研究为海洋环境中叶绿素的动态和分布提供了新的见解.
- 这些发现有助于改善海洋渔业管理和生态监测.
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