Automating Coral Reef Fish Family Identification on Video Transects Using a YOLOv8-Based Deep Learning Pipeline
Domain:
environment and energygeospatialRecord type:
paperdataset
Creator:
GerDi Huy
Host:
Coral reef monitoring in the Western Indian Ocean is limited by the labor demands of underwater visual censuses. This work evaluates a YOLOv8-based deep learning pipeline for automating family-level fish identification from video transects collected in Kenya and Tanzania. A curated dataset of 24 families was tested under different configurations, providing the first region-specific benchmark for automated reef fish monitoring in the Western Indian Ocean. The best model achieved mAP@0.5 of 0.52, with high accuracy for abundant families but weaker detection of rare or complex taxa. Results demonstrate the potential of deep learning as a scalable complement to traditional monitoring methods. Accepted to EUVIP2025, student session
Similar
A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian watersFunctional dynamics of coral reef fish in marine protected areasCommunity-managed coral reef restoration in southern Kenya initiates reef recovery using various artificial reef designsPatterns of juvenile reef fish assemblages across a mangrove-seagrass-coral continuum in Shimoni, KenyaEnergy-use strategies on a degraded coral reef: insights from remote underwater stereo-video and AI-driven 3D trackingCyclone Impacts on Coral Reef Communities in Southwest Madagascar