Yolanda Gonzalez-Cid

Invasive algae, such as Halimeda incrassata, alter marine biodiversity in the Mediterranean Sea. Monitoring these changes over time is crucial for assessing the health of coastal environments and preserving local species. However, this monitoring process is resource-intensive, requiring taxonomic experts and significant amounts of time. Recently, deep learning approaches have attempted to automate the detection of certain seagrass species like Posidonia oceanica and Halophila ovalis from two different strategies: seagrass coverage estimation and detection. This work presents a novel approach to detect Halimeda incrassata and estimate its coverage, independently of the invasion stage of the algae. Two merging methods based on the combination of the outputs of an object detection network (YOLOv5) and a semantic segmentation network (U-net) are developed. The system achieves an F1-scoreof 84.2% and a Coverage Error of 5.9%, demonstrating its capability to accurately detect Halimeda incrassata and estimate its coverage independently of the invasion stage.