Project Background:
The Underwater Image Processing using Deep Learning revolves around exploring the largely uncharted and challenging realm of underwater imaging. With vast oceanic ecosystems and underwater structures to be documented, monitored, and explored, the need for advanced image-processing techniques becomes increasingly essential. Underwater imaging poses unique challenges, such as poor visibility, color distortion, and varying lighting conditions. This offers the promise of unlocking the potential of underwater imagery by enabling automatic image enhancement, object detection, and recognition in these challenging environments. This technology revolutionizes marine biology, environmental monitoring, and offshore oil and gas industries. By harnessing the power of deep learning models, the project seeks to provide more accurate, efficient, and innovative solutions for processing underwater images of oceans and improving the management of underwater resources and ecosystems.
Problem Statement
- The primary problem in underwater image processing is to develop advanced deep learning solutions to enhance, restore, and analyze underwater imagery to extract valuable insights for various applications, including marine biology, environmental conservation, and infrastructure inspection.
- Furthermore, it employs deep learning models for object detection, recognition, and segmentation in underwater imagery, allowing it to identify marine species, archaeological artifacts, and structural anomalies.
- Finally, this problem statement underscores the need for innovative and robust deep-learning models to tackle the unique demands of underwater image processing and analysis.
Aim and Objectives
- To leverage deep learning to develop efficient and accurate methods for processing and analyzing underwater images, enhancing the quality and interpretability of sub-aquatic visual data.
- Develop deep learning models for automatic color correction, contrast adjustment, and noise reduction in underwater images.
- Create systems for automatically detecting and localizing underwater objects, including marine life and submerged structures.
- Enable to recognize and classify marine species from underwater images.
- Design real-time image processing solutions suitable for underwater robotics and monitoring systems deployment.
- Foster advancements in underwater technologies by addressing the unique challenges of underwater image processing.
Contributions to Underwater Image Processing
1. In this project, deep learning enables automatic correction of color distortions, contrast, and noise in underwater images, resulting in clearer and more informative visual data.
2. It contributes to scientific advancements in marine biology, underwater archaeology, and environmental monitoring by providing enhanced data analysis and interpretation tools.
3. Aids in the sustainable management of underwater resources and ecosystems, assisting environmentalists and policymakers in making informed decisions.
Deep Learning Algorithms for Underwater Image Processing
- Convolutional Neural Networks (CNNs)
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U-Net
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Mask R-CNN
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Faster R-CNN
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Autoencoders
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Generative Adversarial Networks (GANs)
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Long Short-Term Memory (LSTM) networks
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Siamese networks for image similarity
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Capsule Networks for object recognition.
Datasets for Underwater Image Processing
- SeaBIRDS Dataset
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PALM-3000 Dataset
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HRF Dataset
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FEMNIST Dataset
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Kaggle National Data Science Bowl
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ROV Dataset
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MSU-Boat Dataset
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ROV-Label Dataset
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Marine Debris Dataset
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Coral Reef Image Dataset (CRID)
Performance Metrics
- Peak Signal-to-Noise Ratio (PSNR)
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Structural Similarity Index (SSIM)
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Mean Absolute Error (MAE)
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Mean Squared Error (MSE)
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Intersection over Union (IoU)
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F1 Score
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Precision and Recall
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Root Mean Square Error (RMSE)
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Cohens Kappa
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
