Automatic coral morphotypes detection with YOLO : a deep learning approach for efficient and accurate coral reef monitoring

Automatic coral morphotypes detection with YOLO : a deep learning approach for efficient and accurate coral reef monitoring Ouassine Y. auteur aut IRD Zahir J. auteur aut IRD Conruyt N. auteur aut IRD Kayal Mohsen auteur aut IRD Martin P. A. auteur aut IRD Chenin Eric auteur aut IRD Bigot L. auteur aut IRD Lebbe R. V. auteur aut IRD text journalArticle eng text/pdf born digital access Coral reefs harbor a large portion of marine biodiversity but are declining rapidly. Conservation efforts rely on monitoring coral abundance and composition as predominant indicators of ecosystem health and management success. However, manual monitoring of coral abundance proves arduous where artificial intelligence-based automatization can help improve efficiency and accuracy. This paper presents a methodology using YOLOv5-based deep learning for automatic detection of corals and classification by morphotype, representing an important step toward streamlining machine-assisted coral reef monitoring. The research addresses the escalating need for precise and timely ecosystem assessments amidst increasing ecological shifts of coral reefs. Using state-of-the-art object detection techniques, the study strives to streamline the detection and classification of diverse coral morphotypes, which is essential for understanding reef dynamics and assessing conservation efforts. To train and evaluate our system, we use a dataset consisting of 280 original underwater coral reef images. We increased the number of annotated images to 388 by manipulating images using data augmentation techniques, which can improve model performance by providing more diverse examples for training. Our system leverages the YOLOv5 algorithm's real-time object detection capabilities, enabling efficient and accurate coral detection. We used YOLOv5 to extract discriminating features from the annotated dataset, enabling the system to generalize, including previously unseen underwater images. The successful implementation of the automatic coral morphotypes detection system with YOLOv5 on our original image dataset highlights the potential of advanced computer vision techniques for coral reef research and conservation. specialized Machine Learning Deep Learning Underwater ecosystems Corals Object Detection YOLOv5 122 082 034 Artificial Intelligence for Knowledge Management, Energy and Sustainability 693 177-188 2024 1868-4238 https://www.documentation.ird.fr/hor/fdi:010094268 10.1007/978-3-031-61069-1_13 1868-4238 [F B010094268] https://www.documentation.ird.fr/hor/fdi:010094268 https://www.documentation.ird.fr/intranet/publi/2025-07/010094268.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2025-07-15 2025-11-06 fdi:010094268 fre