OpenAqua: A Large-Scale Fine-Grained Dataset and Benchmark for Open Underwater Visual Perception

Abstract

Monitoring aquatic biodiversity is vital for maintaining global ecological balance. While advancements in computer vision have revolutionized underwater perception, existing datasets are predominantly limited to coarse-grained categories or lack spatial localization annotations, severely constraining the applicability of models for fine-grained biological identification in real-world scenarios. To address this gap, we introduce OpenAqua, the first large-scale fine-grained dataset dedicated to open underwater visual tasks. OpenAqua is structured around a five-level biological taxonomic hierarchy, comprising 77,970 high-quality images covering 16,540 aquatic species, and providing 132,885 fine-grained bounding boxes and corresponding instance segmentation masks. Based on this dataset, we establish a comprehensive benchmark suite that encompasses not only standard object detection and instance segmentation tasks but also pioneers an underwater open-vocabulary object detection benchmark. Extensive experimental evaluations reveal a significant performance degradation in current models as they progress from coarse-grained to fine-grained recognition. These results highlight the substantial challenges associated with fine-grained semantic perception and domain adaptation in degraded underwater environments. We believe OpenAqua holds the potential to advance fine-grained underwater vision research, facilitate learning from long-tailed distributions, and enable more effective aquatic ecosystem monitoring. Our dataset is available at https://github.com/White-cat-ed/OpenAqua.