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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Rizkie S. Utama
National Research and Innovation Agency

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A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters Raden Sutiadi; Sparisoma Viridi; Giyanto Giyanto; Andarta F. Khoir; Rizkie S. Utama; Elsa D. Aulia; Tri A. Hadi; Ludi Parwadani Aji
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

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Abstract

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.
Co-Authors Andarta F. Khoir Elsa D. Aulia Giyanto Giyanto Ludi Parwadani Aji Raden Sutiadi Sparisoma Viridi Tri A. Hadi