Automating Coral Reef Fish Family Identification on Video Transects Using a YOLOv8-Based Deep Learning Pipeline

TL;DR

This paper develops and evaluates a YOLOv8-based deep learning pipeline to automate coral reef fish family identification from video transects, providing a region-specific benchmark and demonstrating potential for scalable reef monitoring.

Contribution

Introduces a novel deep learning pipeline for automated reef fish family identification and establishes the first region-specific benchmark in the Western Indian Ocean.

Findings

Best model achieved mAP@0.5 of 0.52

High accuracy for abundant families

Weaker detection for rare or complex taxa

Abstract

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.