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Open-world Semantic Segmentation for Underwater Shiphull Inspection

This project implements an open-world semantic segmentation framework for underwater ship hull inspection, adapting the approach from recent advances in autonomous driving to address the unique challenges of underwater imagery analysis.

Overview

The system utilizes a dual-decoder architecture to perform both traditional semantic segmentation and novel defect detection on underwater ship hull imagery. The implementation achieves a mean IoU of 50.9% across 11 classes while maintaining the ability to identify unknown anomalies.

Key Features

  • Dual-decoder architecture with ResNet34 backbone
  • Open-world semantic segmentation capabilities
  • Specialized loss functions for underwater imagery
  • Support for both known defect detection and novel anomaly identification
  • Comprehensive feature space organization through contrastive learning

Dataset

The project uses the LIACI dataset from SINTEF Ocean, which includes:

  • 1893 annotated images
  • 11 semantic classes
  • Diverse underwater conditions and defect types

Classes

  1. Void (background)
  2. Ship hull
  3. Marine growth
  4. Anode
  5. Overboard valve
  6. Propeller
  7. Paint peel
  8. Bilge keel
  9. Defect
  10. Corrosion
  11. Sea chest grating

Requirements

python>=3.8
torch>=1.8.0
torchvision>=0.9.0
numpy
opencv-python
albumentations
tqdm
tensorboard

Project Structure

final_dataset/
├── leftImg8bit/
│   ├── train/
│   ├── val/
│   └── test/
└── gtFine/
    ├── train/
    ├── val/
    └── test/

Implementation Details

Loss Functions

  • Semantic Segmentation Loss (weight: 0.9)
  • Objectosphere Loss (weight: 0.5)
  • MAV Loss (weight: 0.1)
  • Contrastive Loss (weight: 0.5)

Training Configuration

  • Image size: 512×512 pixels
  • Learning rate: 1e-4
  • Optimizer: Adam
  • Batch size: 8
  • Gradient accumulation steps: 4
  • Training epochs: 2000

Performance

Closed-World Performance

Class Category IoU (%)
Ship hull 78.5
Marine growth 81.2
Sea chest grating 84.7
Anode 39.8
Overboard valve 63.4
Paint peel 29.1
Mean IoU 50.03

Open-World Performance

Class Category IoU (%)
Void 78.0
Ship hull 80.0
Sea chest grating 82.0
Propeller 70.0
Mean IoU 50.9

Usage

  1. Dataset Preparation:
python prepare_dataset.py --input_dir /path/to/raw/data --output_dir final_dataset
  1. Training:
python train.py --data_dir final_dataset --batch_size 8 --epochs 2000
  1. Evaluation:
python evaluate.py --model_path /path/to/model --test_dir final_dataset/test

Future Improvements

  1. Dataset Enhancement:

    • Expand dataset with additional rare defect examples
    • Incorporate domain-specific augmentation techniques

  2. Model Improvements:

    • Implement attention mechanisms for varying illumination
    • Add temporal consistency checks
    • Develop confidence estimation metrics

  3. Deployment Optimization:

    • Create lightweight model variants
    • Implement online learning capabilities
    • Optimize for real-time ROV deployment

Citation

@article{underwater_inspection_2024,
  title={Open-world Semantic Segmentation for Underwater Shiphull Inspection},
  author={Badawi, Abubakar Aliyu and Ishfaq-Bhat},
  journal={Department of Marine Technology, NTNU},
  year={2024}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • SINTEF Ocean for providing the LIACI dataset
  • Norwegian University of Science and Technology (NTNU) for research support
  • The authors of the original open-world semantic segmentation paper

Contact

For questions or collaborations, please contact: