Underwater Object Detection Dataset

Classes:

1. Fish (individual and grouped)
2. Crab
3. Human Diver
4. Trash (marine pollution)
5. Jellyfish
6. Coral Reef
7. Sea Turtle
8. Starfish

Dataset Structure:

• Training Set (70%): A robust sample for building detection models.
• Validation Set (10%): Used to fine-tune model performance.
• Test Set (20%): A carefully selected set of images for evaluating model accuracy.

Pre-processing Techniques:

• Auto-Orientation: Ensures all images are correctly aligned.
• Resizing: Images are scaled to 640×640 pixels for uniformity.
• Brightness Normalization: Corrects for underwater lighting conditions.
• Contrast Stretching: Enhances visibility for objects in murky or low-contrast scenes.

New Annotation Techniques:

• Polygonal Segmentation: Introduces more precise segmentation for irregular shapes such as coral reefs.
• 3D Depth Mapping: For enhanced understanding of object placement in underwater space.

Dataset Use Cases:

1. Marine Ecology: Assessing species diversity and tracking the impact of environmental changes.
2. Pollution Analysis: Detecting and classifying marine trash, aiding in cleanup efforts.
3. Underwater Robotics: Training AUVs to recognize and navigate around complex underwater structures like coral reefs or large groups of fish.

Conclusion:

The expanded Underwater Object Detection provides a rich resource for researchers, environmentalists, and engineers working on underwater object detection and classification. Its enhanced classes, precise annotations, and preprocessing techniques make it a valuable asset for developing robust models in marine exploration and conservation.