Coral reefs are home to diverse marine life and provide important environmental and economic benefits. However, they are susceptible to bleaching due to rising water temperatures caused by global warming. Bleaching leads to environmental and economic problems, including increased CO2 levels and difficulty for other marine life to form skeletons. Researchers from Chosun University are developing a machine learning framework to locate bleached coral reefs. They aim to create a model that can withstand the variations in lighting, size, perspective, and background clutter found in photos of maritime environments. The proposed framework combines handmade and deep neural network techniques to extract features and improve classification accuracy.
The Value of AI in Precise Localization of Bleached Corals
Coral reefs are incredibly diverse ecosystems, home to thousands of species of fish and other marine life. However, rising water temperatures due to global warming have led to coral reef bleaching, causing significant environmental and economic problems. Monitoring and surveying marine ecology is crucial to mitigate the consequences of climate change.
Challenges in Monitoring Coral Reefs
Monitoring coral reefs is challenging due to artifacts and ambient noise in underwater images. Discriminating between the target item and the background can be difficult for computer vision systems. Additionally, variations in lighting, size, orientation, perspective, occlusions, and background clutter degrade the performance of localization models.
The Solution: AI and Deep Learning
Researchers from Chosun University are developing a machine learning framework that uses AI and deep learning techniques to locate bleached coral reefs accurately. They aim to overcome the geometric and visual variances found in photos of maritime environments.
Features Extraction and Classification
The framework combines handmade feature extraction methods and deep neural network (DNN) models. DNN models like ResNet, DenseNet, VGGNet, and Inception achieve excellent performance across various applications. However, due to limited bleached examples in existing datasets, overfitting can compromise the robustness of the features. Handmade features, on the other hand, are independent of training data strength but can be impacted by changes in depth, underwater light, and water turbidity.
Bag-of-Hybrid Visual Feature Classification
The suggested framework uses a hybrid approach to extract raw features and then reduces dimensionality and introduces more invariance using the Bag-of-Features (BoF) technique. Local characteristics from the picture are used to improve photometric invariance. The use of BoF also reduces complexity and storage requirements. After extensive experimentation, the researchers have determined the optimal patch, cluster size, kernel combination, and classifier.
Benefits for Businesses
If you want to evolve your company with AI and stay competitive, utilizing the machine learning framework developed by Chosun University researchers can provide several advantages:
- Improved accuracy in localizing bleached coral reefs
- Optimized monitoring and surveying of marine ecology
- Enhanced understanding of the impacts of climate change
- Potential for identifying new medicinal substances and treatments
To learn more about the research conducted by Chosun University researchers, click here.
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