Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8

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Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8

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📡 Production-grade maritime surveillance system for detecting ships in Synthetic Aperture Radar (SAR) imagery using YOLOv8-OBB (Oriented Bounding Boxes) and Lee speckle filtering. Handles the unique challenges of radar imagery: multiplicative noise and arbitrarily-oriented vessels.

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# Trident - SAR Maritime Vessel Detection

A production-grade maritime surveillance system for detecting ships in Synthetic Aperture Radar (SAR) imagery using Oriented Bounding Boxes (OBB) and speckle noise filtering.

![Detection Results](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/detection_grid.jpg)

## Mission

Detect ships in SAR images where vessels appear as noisy "salt-and-pepper" clusters, using:
- **Lee Speckle Filter**: Remove radar-specific multiplicative noise
- **YOLOv8-OBB**: Detect ships with rotated bounding boxes (ships are rarely horizontal)

## Pipeline

![Pipeline](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/pipeline.jpg)

## Why This Approach?

### The SAR Challenge
SAR images suffer from "speckle noise" - a granular interference pattern unique to coherent radar imaging. Ships appear as bright clusters mixed with noise, making standard detection difficult.

### Speckle Filtering (The "Secret Sauce")

![Speckle Filter Comparison](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/speckle_filter_comparison.jpg)

The Lee filter removes multiplicative radar noise while preserving ship edges:

```
Before Lee Filter:          After Lee Filter:
░░█░░█░░░░█░░█░░           ░░░░░░░░░░░░░░░░
░█░█░█░░░█░█░░░░           ░░░████████░░░░░
░░█░░░█░█░░█░░░░    →      ░░░████████░░░░░
░█░█░░░█░░█░░░░░           ░░░████████░░░░░
░░░█░█░░░░░█░░░░           ░░░░░░░░░░░░░░░░
```

### Why Oriented Bounding Boxes?
Ships sail in arbitrary directions. Axis-aligned boxes waste pixels and overlap:

```
Standard Box (AABB):        Oriented Box (OBB):
┌─────────────────┐            ╱████████████╲
│  ████████       │           ╱████████████╲
│       ████████  │    →       ╲████████████╱
│           █████ │            ╲████████████╱
└─────────────────┘
  60% background!              Tight fit!
```

## Sample Detection Results

| Single Ship | Multiple Ships | Complex Scene |
|:-----------:|:--------------:|:-------------:|
| ![](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/detection_1.jpg) | ![](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/detection_2.jpg) | ![](https://raw.githubusercontent.com/kbhujbal/Trident-synthetic_aperture_radar_maritime_vessel_detection_yolov8/main/assets/detection_3.jpg) |

## Project Structure

```
Trident/
├── src/
│   ├── __init__.py          # Package initialization
│   ├── data_manager.py      # Kaggle download, COCO→YOLO conversion
│   ├── preprocessing.py     # Lee speckle filter implementation
│   ├── train.py             # YOLOv8-OBB training pipeline
│   └── inference.py         # Detection and visualization
├── config/
│   └── config.yaml          # Configuration parameters
├── scripts/
│   └── generate_samples.py  # Generate sample outputs
├── assets/                  # README images
├── data/                    # Dataset storage
├── output/                  # Detection results
└── requirements.txt
```

## Quick Start

### 1. Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/Trident.git
cd Trident

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
```

### 2. Kaggle Setup

```bash
# Configure API credentials
# 1. Go to kaggle.com → Settings → Create New API Token
# 2. Create credentials file:
mkdir -p ~/.kaggle
echo '{"username":"YOUR_USERNAME","key":"YOUR_API_KEY"}' > ~/.kaggle/kaggle.json
chmod 600 ~/.kaggle/kaggle.json
```

### 3. Download & Prepare Dataset

```bash
# Download SSDD from Kaggle and prepare YOLO OBB format
python src/data_manager.py --download --prepare --data-root ./data
```

### 4. Train the Model

```bash
# Train with default settings (YOLOv8n-OBB)
python src/train.py --data ./data/ssdd_yolo_obb/data.yaml

# Train with custom settings
python src/train.py \
    --data ./data/ssdd_yolo_obb/data.yaml \
    --model yolov8s-obb \
    --epochs 150 \
    --batch 32 \
    --imgsz 640
```

### 5. Run Inference

```bash
# Demo mode (random validation images)
python src/inference.py \
    --model ./trident_sar/ship_detection/weights/best.pt \
    --data ./data/ssdd_yolo_obb/data.yaml \
    --output ./output

# Single image
python src/inference.py \
    --model ./trident_sar/ship_detection/weights/best.pt \
    --source /path/to/sar_image.jpg \
    --output ./output
```

## Configuration

Edit `config/config.yaml` to customize:

```yaml
preprocessing:
  lee_filter:
    window_size: 7        # Larger = more smoothing

model:
  variant: "yolov8n-obb"  # n/s/m/l/x variants
  image_size: 640

training:
  epochs: 100
  batch_size: 16
  learning_rate: 0.001
```

## Model Variants

| Model | Size | Speed | Accuracy |
|-------|------|-------|----------|
| yolov8n-obb | 3.2M | Fastest | Good |
| yolov8s-obb | 11.2M | Fast | Better |
| yolov8m-obb | 25.9M | Medium | High |
| yolov8l-obb | 43.7M | Slow | Higher |
| yolov8x-obb | 68.2M | Slowest | Highest |

## API Usage

```python
from src.preprocessing import SARPreprocessor, apply_lee_filter
from src.inference import SARShipDetector

# Preprocess a SAR image
preprocessor = SARPreprocessor(lee_window_size=7)
filtered = preprocessor.process(sar_image)

# Detect ships
detector = SARShipDetector(
    model_path="trident_sar/ship_detection/weights/best.pt",
    confidence_threshold=0.25
)
result = detector.detect("path/to/sar_image.jpg")

print(f"Ships detected: {result['count']}")
for det in result['detections']:
    print(f"  Confidence: {det['confidence']:.2f}")
    print(f"  Angle: {det['angle']:.1f}°")
```

## Output Format

Detection results include:
- `corners`: 4 corner points of the rotated box
- `center`: Center coordinates
- `width`, `height`: Box dimensions
- `angle`: Rotation angle in degrees
- `confidence`: Detection confidence

## Dataset: SSDD

The SAR Ship Detection Dataset (SSDD) used in this project:
- **Source**: [Kaggle - SARscope Maritime Images](https://www.kaggle.com/datasets/kailaspsudheer/sarscope-unveiling-the-maritime-landscape)
- **Train**: 4,716 images
- **Validation**: 1,346 images
- **Format**: COCO JSON (converted to YOLO OBB)

## Technical Details

### Lee Filter Algorithm

The Lee filter assumes multiplicative noise: `I = R × N`

```
R̂ = μ + W × (I - μ)
W = σ² / (σ² + σ_noise²)
```

Where:
- `μ` = local mean
- `σ²` = local variance
- `W` = adaptive weight (preserves edges)

### OBB Output Format

YOLO OBB format: `class x1 y1 x2 y2 x3 y3 x4 y4`

Four normalized corner coordinates defining the rotated rectangle.

## License

MIT License - see [LICENSE](LICENSE) for details.

## Acknowledgments

- [SSDD Dataset](https://www.kaggle.com/datasets/kailaspsudheer/sarscope-unveiling-the-maritime-landscape) creators
- [Ultralytics](https://github.com/ultralytics/ultralytics) YOLOv8 team
- Jong-Sen Lee (Lee filter inventor, 1981)

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