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August 23, 2025Indonesian Journal of Information SystemsOpen Access

Coral Detection based on Optimised Lightweight YOLO Model

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Authors

RSRaymond Erz SaragihHHHusna Sarirah HusinMMMuhammad Khairul Naim Mursalim

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Overview

Deep learning reveals improved coral detection accuracy, indicating potential for ocean conservation efforts.

Key Points

  • The proposed YOLOv8 model achieved a mAP of 55.88%, enhancing detection efficiency.
  • Precision and recall metrics were central in evaluating the optimized detection model.
  • Training was conducted on a specialized underwater coral dataset to refine detection accuracy.
  • The lightweight design supports practical applications in marine conservation, addressing significant threats to coral reefs.

Cite This Study

Saragih et al. (2025) studied this question.

synapsesocial.com/papers/68af5bb6ad7bf08b1eadf4d2https://doi.org/10.24002/ijis.v8i1.11628
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Coral-YOLO: An Intelligent Optical Vision Sensing Framework for High-Fidelity Marine Habitat Monitoring and Forecasting2025 · 3 citations
  2. 2Fine-Grained Analysis of Coral Instance Segmentation using YOLOv8 Models2024 · 6 citations
  3. 3YOLO-Driven Automated Detection of Coral Reef Health Indicators in Underwater Imagery2022 · 2 citations
  4. 4Harnessing Computer Vision and Deep Learning to Monitor Coral Reef Health2025 · 4 citations
  5. 5Coral morphology detection in underwater imagery using YOLOv12 with CNN and transformer encoder fusion2026 · 1 citations