<
Semantic Segmentation of Underwater Imagery
CV
This project focuses on developing a multi-class segmentation algorithm for underwater images.
The U-Net model was used and enhanced by incorporating a dropout mechanism to mitigate overfitting.
Various images manipulation techniques were applied to calculate database statistics and to improve the data through preprocessing and augmentation.
Project developed for Machine Learning for CV course at ESILV.
The U-Net model was used and enhanced by incorporating a dropout mechanism to mitigate overfitting.
Various images manipulation techniques were applied to calculate database statistics and to improve the data through preprocessing and augmentation.
Project developed for Machine Learning for CV course at ESILV.
Python
Python
OpenCV
OpenCV
PyTorch
PyTorch
MatplotLib
MatplotLib
NumPy
NumPy
Computer Vision
Computer Vision
Segmentation models
Segmentation models
Semantic-Segmentation-of-Underwater-Imagery
A segmentation project done as the final project of the Machine Learning for Computer Vision course at ESILV
A detailed presentation can be found in this repo at Project_presentation.pdf
Example of output
Introduction
What is Semantic Segmentation?
- Definition: Label each pixel of an image with a class/category.
- Objective: Segment underwater images into objects (e.g., fish, reefs, plants).
- Goal: Automatically label each pixel for underwater imagery analysis.
Problem Statement
Illustration of the Problem
- Challenges in segmenting underwater imagery.
- Importance of precise segmentation in marine applications.
Project Goals
- Train a segmentation model to identify underwater objects.
- Generate segmentation masks with pixel-wise classification.
Dataset Overview
Structure
- Train/Validation: 1525 paired samples.
- Test: 110 paired samples for benchmarking.
Object Categories
| Category | RGB Code |
|---|---|
| Fish | Yellow: 110 |
| Reefs | Pink: 101 |
| Aquatic Plants | Green: 010 |
| Wrecks/Ruins | Sky: 011 |
| Human Divers | Blue: 001 |
| Robots | Red: 100 |
| Sea-floor | White: 111 |
| Background | Black: 000 |
Methodology
Segmentation Model
- Model: U-Net architecture.
- Enhancements: Dropout layers to prevent overfitting.
Data Augmentation
- Techniques: Horizontal flipping, rotation, Gaussian noise, etc.
Training Details
Metrics
- Dice Loss: Measures overlap between predicted and ground truth masks.
- IoU: Evaluates the intersection over union for object regions.
Optimizer & Scheduler
- Adam Optimizer: Learning rate = 1e-4.
- Scheduler: Reduces learning rate when validation loss plateaus.
Results
Training & Validation Loss
- Steady decline over 50 epochs.
- Minimal gap between training and validation loss.
IoU Metrics
- Significant improvement in training and validation IoU.
Future Directions
- Hyperparameter Tuning: Optimize learning rates and batch sizes.
- Dataset Expansion: Incorporate similar datasets.
- Transfer Learning: Use pretrained models for better feature extraction.
- Advanced Augmentation: Focus on underrepresented classes.
Applications
- Marine Ecology: Track underwater species.
- Archaeology: Identify submerged historical sites.
- AUVs: Enhance underwater navigation.
- Environmental Monitoring: Assess vegetation health and pollution.
Thank You!
ML-Powered Suggestions
i
How it works:
I use a MiniLM-L6 transformer model to turn project descriptions into mathematical vectors.
By calculating the Cosine Similarity between these vectors, the model identifies semantically related work.
Analyzing project similarities...