Real-time-Coral-Reefs-Monitoring-and-Protection-using-Computer-Vision-A-Case-Study-on-COTS-Detection

Coral reefs, among the most valuable and biodiverse ecosystems on the planet, are under severe threat due to climate change, pollution, and various anthropogenic activities. The alarming decline in reef health necessitates the adoption of innovative and data-driven restoration approaches. Recent advancements in machine learning (ML) and computer vision offer promising solutions for ecological monitoring and restoration planning. This study explores the application of YOLO V5 (You Only Look Once Version 5), a real-time object detection algorithm, for identifying Crown-of-Thorns Starfish (COTS)—a major predator contributing to coral degradation.

By leveraging YOLO V5’s capabilities in image recognition and rapid detection, the research aims to automate the process of monitoring reef health and detecting invasive species, which is essential for effective and timely restoration strategies. The integration of ML and computer vision in coral reef management can significantly improve the identification of restoration sites, optimize resource deployment, and enhance the monitoring of coral health over time.

This project highlights the potential of using state-of-the-art deep learning models to support marine conservation efforts. The findings demonstrate that computer vision technologies like YOLO V5 can play a vital role in the sustainable management and protection of coral ecosystems. Ultimately, incorporating ML into reef restoration not only enhances ecological insights and operational efficiency but also contributes to the long-term resilience and sustainability of marine environments.