Automating Coral Reef Fish Family Identification on Video Transects Using a YOLOv8-Based Deep Learning Pipeline
This work addresses the labor-intensive problem of coral reef monitoring for researchers and conservationists in the Western Indian Ocean, though it is incremental as it applies an existing method to new data.
The paper tackled automating coral reef fish family identification from video transects in the Western Indian Ocean using a YOLOv8-based deep learning pipeline, achieving a mAP@0.5 of 0.52 with high accuracy for abundant families but weaker detection of rare taxa.
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.