Automatic deep learning-based pipeline for Mediterranean fish segmentation
Caterina Muntaner-González, Antonio Nadal-Martínez, Miguel Martín-Abadal, Yolanda González-Cid
Abstract
Climate change and human activities are altering the Mediterranean marine biodiversity. Monitoring these alterations 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. To address this, we present an automated pipeline that detects, classifies and segments 17 species of Mediterranean fish using YOLOv8, integrated into an underwater stereo vision system capable of real-time inference and selective data storage. The proposed model demonstrates strong performance in detecting, classifying, and segmenting 17 Mediterranean fish species, achieving an mAP50(B) of 0.886 and an mAP50(M) of 0.889.