构建越南水生无脊椎动物数据集，助力生态监测与物种识别
SuoiAI: Building a Dataset for Aquatic Invertebrates in Vietnam
- 采用半监督学习减少标注工作量，结合目标检测与分类模型
- 建立端到端流程，解决数据稀缺与细粒度分类难题
- 适合生态学研究者、生物多样性保护及自动化监测应用
理解与监测水生生物多样性对生态保护和 conservation 至关重要。本文提出 SuoiAI，一个针对越南水生无脊椎动物的端到端数据集构建流程，并利用机器学习技术实现物种分类。我们阐述了数据采集、标注与模型训练的方法，重点通过半监督学习降低标注成本，并利用最先进的目标检测与分类模型。该方法旨在应对数据稀缺、细粒度分类及复杂环境部署等挑战。
原文摘要 · Abstract (English)
Understanding and monitoring aquatic biodiversity is critical for ecological health and conservation efforts. This paper proposes SuoiAI, an end-to-end pipeline for building a dataset of aquatic invertebrates in Vietnam and employing machine learning (ML) techniques for species classification. We outline the methods for data collection, annotation, and model training, focusing on reducing annotation effort through semi-supervised learning and leveraging state-of-the-art object detection and classification models. Our approach aims to overcome challenges such as data scarcity, fine-grained classification, and deployment in diverse environmental conditions.
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