Proceedings Article | 30 September 2024
KEYWORDS: Education and training, Image segmentation, RGB color model, Image processing, Neural networks, Data modeling, Deep learning, Machine learning, Color image segmentation, Image classification, Deep convolutional neural networks
Graph Convolutional Networks (GCNs) offer an innovative method for classifying and identifying segmented images by converting them into superpixels, which serve as nodes in a characteristic graph for each image. This deep learning algorithm generates graph structures with edges representing the distance and color intensity relationship, ensuring accurate representation of image content during training.
GCNs are particularly effective for sea turtle identification due to the unique morphology and patterns of these animals, such as the size, structure, and coloration of their head, carapace, flippers, and characteristic scutes. By leveraging these consistent patterns, GCNs can generate detailed graphs from each turtle, facilitating precise monitoring and research. This includes evaluating geographic distribution, population density, migration, survival, reproduction, and growth of sea turtles.
The proposed GCN algorithm for sea turtle identification is robust and versatile, demonstrating high performance in terms of precision and accuracy. It is resilient to variations in images taken in different environments, angles, and lighting conditions. This marks a significant improvement over other algorithms like Hotspotter by automating the entire process and using segmentation to simplify images and leveraging turtle morphology to generate the graph. Additionally, the use of different color channels allows for the use of images from various environments, reducing the need for specific image characteristics for training. This new algorithm supports the monitoring and protection of sea turtles by providing a powerful tool for their conservation and mapping, contributing significantly to the broader understanding of marine life.
