DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring

• Published in: PeerJ (San Francisco, CA) Vol. 11; p. e16219
• Main Authors: Furtado, Daniel P., Vieira, Edson A., Nascimento, Wildna Fernandes, Inagaki, Kelly Y., Bleuel, Jessica, Alves, Marco Antonio Zanata, Longo, Guilherme O., Oliveira, Luiz S.
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ, Inc 06.11.2023; PeerJ Inc
• Subjects: Algae; Algorithms; Anthropogenic factors; Artificial intelligence; Automation; Autonomous underwater vehicles; Classification; Computer vision; Convolutional neural network; Coral reefs; Corals; Data Mining and Machine Learning; Data Science; Datasets; Deep learning; Endosymbionts; Environmental monitoring; Image processing; Information processing; Machine learning; Marine Biology; Marine ecology; Marine environment; Semantics; Social networks; Taxonomy
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.16219

Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts. Most coral monitoring activities involve underwater photographs, which can be costly to generate on large spatial scales and require processing and analysis that may be time-consuming. The Marine Ecology Laboratory (LECOM) at the Federal University of Rio Grande do Norte (UFRN) developed the project “#DeOlhoNosCorais” which encourages users to post photos of coral reefs on their social media (Instagram) using this hashtag, enabling people without previous scientific training to contribute to coral monitoring. The laboratory team identifies the species and gathers information on coral health along the Brazilian coast by analyzing each picture posted on social media. To optimize this process, we conducted baseline experiments for image classification and semantic segmentation. We analyzed the classification results of three different machine learning models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The best results were achieved by combining EfficientNet for feature extraction and Logistic Regression for classification. Regarding semantic segmentation, the U-Net Pix2Pix model produced a pixel-level accuracy of 86%. Our results indicate that this tool can enhance image selection for coral monitoring purposes and open several perspectives for improving classification performance. Furthermore, our findings can be expanded by incorporating other datasets to create a tool that streamlines the time and cost associated with analyzing coral reef images across various regions.