Journal Article

SCSFish2025: a large dataset from South China sea for coral reef fish identification

Wang, Ying, Gao, Yang, Wang, Meng, Xiao, Wei, Zheng, Fudan, Jia, Houlei, Chen, Zhiguang

2025

Overview

Coral reefs are one of the most biodiverse ecosystems on Earth and are extremely important for marine ecosystems. However, coral reefs are rapidly degrading globally, and for this reason, in-situ online monitoring systems are being used to monitor coral reef ecosystems in real time. At the same time, artificial intelligence technology, particularly deep learning technology, is playing an increasingly important role in the study of coral reef ecology, especially in the automatic detection and identification of coral reef fish. However, deep learning is essentially a data-driven technique that relies on high-quality datasets for training, while existing fish identification datasets suffer from low resolution and inaccurate labeling, which limits the application of deep learning techniques to coral reef fish identification. To better utilize deep learning techniques for real-time automatic detection and identification of coral reef fish from the data collected by the in-situ online monitoring system, this paper proposes a high-resolution, fish species-rich, and well-labeled coral reef fish dataset SCSFish2025, which is the first publicly available coral reef fish dataset in the waters of China’s Nansha Islands. SCSFish2025 contains 11,956 high-resolution underwater surveillance images and over 120,000 bounding boxes covering 30 species of fish that have been manually labelled by experienced fish identification experts, with sub-category labels for blurring, occlusion, and altered pose. Furthermore, this paper establishes a benchmark for the dataset by analyzing the detection performance of deep learning object detection techniques on this dataset using four state-of-the-art or typical object detection models as baseline models. The best baseline model RT-DETRv2 achieves mAP@50 performance of 0.9960 and 0.7486 respectively on the five-fold cross-validation of the training set and the independent test set. The release of this dataset will help promote the development of AI technology in the study of automatic detection and identification of coral reef fish, and provide strong support for the study of marine biodiversity and ecosystems. The project code and dataset are available at https://github.com/FudanZhengSYSU/SCSFish2025 .

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/158/2446/837 / 639/705/117 / 704/829/826 / Animals / Annotations / Artificial intelligence / Biodiversity / China / Classification / Climate change / Coral reef ecosystems / Coral reef fish identification / Coral Reefs / Dataset / Datasets / Deep Learning / Ecosystem / Ecosystems / Environmental Monitoring - methods / Fish / Fisheries / Fishes - classification / Fishes - physiology / Humanities and Social Sciences / Identification / Libraries / Marine ecology conservation / Marine ecosystems / Monitoring systems / multidisciplinary / Object detection technique / Science / Science (multidisciplinary) / Workloads