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基于多模式学习的藻类类型识别,使用图像和粒子模式

Do Hyuck Kwon¹, Min Jun Lee², Heewon Jeong³

¹Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

|January 26, 2025

概括

一种人工智能 (AI) 多模式方法有效地识别水处理中的藻类类. 这种人工智能方法整合了藻类图像和粒子特性,改善了水质和供应安全.

科学领域:

环境科学环境科学
水处理技术水处理技术
环境监测中的人工智能

背景情况:

淡水系统中的藻类繁殖因城市化和气候变化而加剧,使水处理复杂化.
有效识别占主导地位的藻类物种对于保持水质和确保安全饮用水供应至关重要.
传统的藻类识别方法通常很慢,需要专门的专业知识.

研究的目的:

引入和评估基于人工智能 (AI) 的多模式方法,以加强水处理中的藻类识别.
整合藻类图像和粒子特性,以便对藻类类型进行可靠和可靠的分类.
评估AI模型在识别关键藻类物种中的性能和可解释性.

主要方法:

采用了多模式学习方法,将藻类图像和通过FlowCam获得的粒子特性结合起来.
采用早期,晚期和混合融合技术来整合多模式数据集.
应用可解释的AI方法 (SHAP,Grad-CAM) 来理解特征贡献.

主要成果:

具有晚期融合的多模态藻类识别器获得了高F1分:培训0.91和测试0.88.
图像和粒子数据模式都显示出作为深度学习算法的互补组件的巨大潜力.
人工智能方法被证明是强大可靠的,用于分类主要藻类类型,如Anabaena,Microcystis和Synedra.

结论:

开发的AI多模式方法为水处理中快速准确的藻类识别提供了重大进步.
整合不同的数据模式提高了藻类分类的可靠性,有助于改善水质管理.
这种人工智能驱动的方法支持通过有效监测主导藻类物种来实现安全和清洁的供水目标.

关键词:

藻类藻类是一种藻类.深度学习 (Deep Learning) 是一种深度学习.多式联运多式联运多模态藻类识别器多模态藻类识别器

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