Underwater Coral Reef Species Identification Dataset

In the context of global environmental changes, coral reefs, as an important part of the marine ecosystem, face severe survival challenges. Traditional coral reef survey methods are lengthy, costly, and may have high misjudgment rates. The Underwater Coral Reef Species Identification Dataset aims to enhance the automation of species identification by providing a large amount of high-quality image data. The dataset includes high-resolution underwater coral reef images collected from various global sea areas, taken by divers using professional underwater photography equipment under natural and artificial lighting conditions. Measures to ensure data quality include multiple rounds of annotation and expert review, with an annotation team of 10 experts with backgrounds in marine biology. Image enhancement and denoising techniques were used in the data processing, and all data is stored in JPG format and organized by species. The core advantage of this dataset lies in the ultra-high annotation precision and consistency, with an annotation accuracy rate of over 98%. By introducing a deep learning-based automatic correction annotation method and multi-view imaging technology, this dataset demonstrates higher accuracy and stability against external interference compared to similar products, significantly increasing task identification efficiency by 30%. Additionally, this dataset includes information on more than 50 coral species, making it the largest coral reef species image collection globally, widely applicable in various scientific research and environmental protection projects, with strong scalability and versatility.