Cold-water coral (CWC) reefs, such as those formed by Desmophyllum pertusum and Madrepora oculata, are vital yet vulnerable marine ecosystems (VMEs). The need for accurate and efficient monitoring of these habitats has driven the exploration of innovative approaches. This study presents a novel application of the YOLOv8l-seg deep learning model for the automated detection and segmentation of these key CWC species in underwater imagery. The model was trained and validated on 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, enabling the assessment of coral cover and spatial distribution. The study revealed significant variability in coral cover 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 underscores 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 ultimately contributing to the development of effective conservation strategies.

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.