SuoiAI: Building a Dataset for Aquatic Invertebrates in Vietnam
This work addresses data scarcity and fine-grained classification for ecological conservation in Vietnam, but it appears incremental as it applies existing methods to a new dataset.
The paper tackles the problem of monitoring aquatic biodiversity by proposing SuoiAI, an end-to-end pipeline to build a dataset of aquatic invertebrates in Vietnam and use machine learning for species classification, with a focus on reducing annotation effort through semi-supervised learning and leveraging state-of-the-art models.
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.