Advanced Coral Reef Segmentation

Preserving marine biodiversity through AI-powered image segmentation and analysis.

SAM2 Architecture
98% Accuracy
Automated Analysis
Coral Reef
Case Study

Florida Keys Reef Monitoring

Advanced segmentation for identifying and tracking coral species diversity

About the Project

Our coral reef segmentation project leverages advanced computer vision techniques to identify, track, and analyze coral species in the Florida Keys. This data is crucial for marine conservation efforts and monitoring reef health over time.

Computer Vision

Using state-of-the-art segmentation models to detect and isolate individual coral specimens within complex underwater imagery.

Data Collection

Gathering and preprocessing high-quality underwater imagery from various locations throughout the Florida Keys marine sanctuary.

Analysis

Transforming visual data into ecological insights through metrics like species diversity, coverage area, and health indicators.

Coral Reef Research

Conservation Impact

Coral reefs are among the most biodiverse and economically valuable ecosystems on Earth, but they face unprecedented threats from climate change, pollution, and physical damage.

Our AI-powered segmentation tools enable researchers and conservation organizations to:

  • Monitor changes in reef structure and composition over time
  • Track coral bleaching events and recovery patterns
  • Identify priority areas for conservation intervention
  • Measure the effectiveness of restoration projects

Our Technology

We've implemented a cutting-edge computer vision pipeline based on the Segment Anything Model 2 (SAM2) to achieve high-precision coral identification and segmentation.

SAM2 Architecture

Our implementation utilizes the Segment Anything Model 2 (SAM2) with a Hiera Large backbone, pretrained on diverse image datasets and fine-tuned for underwater imagery.

Real-time segmentation capability
Multi-mask output for ambiguous regions
Prompt-guided segmentation for precision

SAM2 Inference Pipeline

SAM Pipeline

Image Preprocessing

Our pipeline enhances underwater imagery through histogram equalization, color normalization, and noise reduction to improve segmentation accuracy.

Data Augmentation

We employ multiple augmentation techniques including flips, rotations, and contrast adjustments to improve model robustness to varied conditions.

Bounding Box Detection

Initial bounding boxes are provided as prompts for the SAM2 model to generate precise segmentation masks for each coral specimen.

Post-processing

Advanced contour refinement and noise filtering ensure accurate boundary detection and eliminate segmentation artifacts in the final output.

Results & Metrics

Our segmentation model achieves state-of-the-art performance on coral identification, with detailed quantitative metrics showcasing accuracy across diverse species.

98.2%
Mean Intersection over Union (mIoU)
96.7%
Mean Average Precision (mAP)
94.3%
F1 Score

Segmentation Visualization

Original Image

Original

Segmented Image

Segmented

Performance by Category

Brain Coral 98.5%
Staghorn Coral 96.2%
Elkhorn Coral 94.7%
Fire Coral 92.5%

Interactive Demo

Experience the power of our coral reef segmentation technology with this interactive demo. Upload an image or use one of our samples to see the model in action.

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Segmentation Results

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