混合深度学习架构用于细粒度的海洋微藻类的分类
Jiaxuan Li1, Min Fu1, Lichi Ding2
1College of Electronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China.
The Science of the total environment
|August 15, 2025
概括
新型深度学习模型HySwinFormer准确地识别了微藻类物种,这对于海洋监测至关重要. 这种高效的系统实现了高性能和实时分析,使生态管理中的实际应用成为可能.
科学领域:
- 海洋生物学 海洋生物学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 准确的微藻识别对于海洋生态监测和环境管理至关重要.
- 由于微藻物种之间的形态相似性和生物复杂性,现有方法在分类方面面临挑战.
研究的目的:
- 开发一个强大的深度学习架构,用于细粒度微藻类图像分类.
- 解决当前方法在准确区分相似的微藻物种中的局限性.
主要方法:
- 提出了HySwinFormer,这是一个双分支深度学习架构,具有多尺度建模分支 (MSMB) 和细节增强分支 (DEB).
- 整合了一个增强的高效多尺度注意力 (EMA) 模块,以改善特征歧视.
- 在20种瑞索索莱尼亚物种的定制数据集和两个公共基准 (浮游生物集-1.0,WHOI-浮游生物) 上评估了该模型.
主要成果:
- 在准确性,F1得分和马修斯相关系数 (MCC) 方面,HySwinFormer表现出比现有模型更好的性能.
- 该模型在公共浮游生物数据集上表现出强大的概括能力.
- 在NVIDIA Jetson边缘计算平台上部署时,可实现大约25 FPS的实时分析.
结论:
- HySwinFormer为微藻识别提供了一种有效可靠的解决方案.
- 该模型的效率和实时处理能力验证了其在资源有限的环境中用于智能海洋生态监测系统的实际可行性.
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