Abstract:
Classification of coral reefs in uncontrolled underwater images plays a crucial role in the
examination of marine biodiversity and efforts for conservation. This study proposes a
sophisticated methodology employing deep learning techniques to enhance the precision
and effectiveness of coral classification. More precisely, personalized ResNet models
combined with multi-head attention and one-shot learning were created, alongside the
utilization of YOLOv8 for object detection and segmentation tasks.
The effectiveness of the approach was assessed on the Eilat, Eilat2, and RSMAS datasets,
displaying notable enhancements in classification accuracy. ResNet models, which were
pretrained on the Eilat2 dataset, displayed exceptional accuracy in the identification of
coral species. YOLOv8 was employed for segmentation purposes, effectively outlining
individual coral formations. The amalgamation of these advanced deep learning techniques
substantially improved the precision and dependability of coral classification.
The findings of this study contribute to the progression of coral reef conservation initiatives
and investigations into marine biodiversity by offering robust tools for monitoring coral
well-being and diversity. The prosperous application of instance segmentation, supported
by Convolutional Neural Networks (CNNs) and Vision Transformers, showcases a high
potential for generalization and robust performance in practical underwater imagery.
