Version 1
: Received: 9 August 2024 / Approved: 10 August 2024 / Online: 12 August 2024 (04:21:34 CEST)
How to cite:
Gayá-Vilar, A.; Abad-Uribarren, A.; Rodríguez-Basalo, A.; Ríos, P.; Cristobo, J.; Prado, E. Deep-Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8. Preprints2024, 2024080740. https://doi.org/10.20944/preprints202408.0740.v1
Gayá-Vilar, A.; Abad-Uribarren, A.; Rodríguez-Basalo, A.; Ríos, P.; Cristobo, J.; Prado, E. Deep-Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8. Preprints 2024, 2024080740. https://doi.org/10.20944/preprints202408.0740.v1
Gayá-Vilar, A.; Abad-Uribarren, A.; Rodríguez-Basalo, A.; Ríos, P.; Cristobo, J.; Prado, E. Deep-Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8. Preprints2024, 2024080740. https://doi.org/10.20944/preprints202408.0740.v1
APA Style
Gayá-Vilar, A., Abad-Uribarren, A., Rodríguez-Basalo, A., Ríos, P., Cristobo, J., & Prado, E. (2024). Deep-Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8. Preprints. https://doi.org/10.20944/preprints202408.0740.v1
Chicago/Turabian Style
Gayá-Vilar, A., Javier Cristobo and Elena Prado. 2024 "Deep-Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8" Preprints. https://doi.org/10.20944/preprints202408.0740.v1
Abstract
Cold-water coral (CWC) reefs represent vulnerable marine ecosystems (VMEs) of critical ecological importance. This study presents a novel approach for the automated detection and segmentation of key CWC species, Desmophyllum pertusum and Madrepora oculata, in underwater imagery using the YOLOv8l-seg deep learning model. The model was applied to images collected at two Natura 2000 sites in the Cantabrian Sea: the Avilés Canyon System (ACS) and El Cachucho Seamount (CSM). Results demonstrate the model's high accuracy in identifying and delineating individual coral colonies. Significant variability in coral cover was observed between and within the study areas, highlighting the patchy nature of CWC habitats. Three distinct coral community groups were identified based on percentage coverage composition and abundance, with the highest coral cover group being located exclusively in the La Gaviera canyon head within the ACS. This research demonstrated the potential of deep learning models for efficient and accurate monitoring of VMEs, facilitating the acquisition of high-resolution data essential for understanding CWC distribution, abundance, and community structure, and contributing to the development of effective conservation strategies.
Keywords
Cold-water corals; Underwater image processing; Deep learning; Marine protected areas; Aviles Canyon System; El Cachucho
Subject
Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
