  Coral reefs are vital ecosystems that are under increasing threat due to
local human impacts and climate change. Efficient and accurate monitoring of
coral reefs is crucial for their conservation and management. In this paper, we
present an automatic coral detection system utilizing the You Only Look Once
(YOLO) deep learning model, which is specifically tailored for underwater
imagery analysis. To train and evaluate our system, we employ a dataset
consisting of 400 original underwater images. We increased the number of
annotated images to 580 through image manipulation using data augmentation
techniques, which can improve the model's performance by providing more diverse
examples for training. The dataset is carefully collected from underwater
videos that capture various coral reef environments, species, and lighting
conditions. Our system leverages the YOLOv5 algorithm's real-time object
detection capabilities, enabling efficient and accurate coral detection. We
used YOLOv5 to extract discriminating features from the annotated dataset,
enabling the system to generalize, including previously unseen underwater
images. The successful implementation of the automatic coral detection system
with YOLOv5 on our original image dataset highlights the potential of advanced
computer vision techniques for coral reef research and conservation. Further
research will focus on refining the algorithm to handle challenging underwater
image conditions, and expanding the dataset to incorporate a wider range of
coral species and spatio-temporal variations.
