A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

Raden Sutiadi (Institute Technology Bandung)
Sparisoma Viridi (Institute Technology Bandung)
Giyanto Giyanto (National Research and Innovation Agency)
Andarta F. Khoir (National Research and Innovation Agency)
Rizkie S. Utama (National Research and Innovation Agency)
Elsa D. Aulia (National Research and Innovation Agency)
Tri A. Hadi (National Research and Innovation Agency)
Ludi Parwadani Aji (Naturalis Biodiversity Center)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 43, No 2: August 2026

Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.