Automatic Classification of Coral Reef Model based on Deep Learning Method for Underwater Images - Promnukul, Piyapat; Vijayasarveswari, V.; Mohd Yacob, Yasmin; Bachok, Zainudin (Advanced and Sustainable Technologies (ASET), 2026)
Coral reefs play a vital role in maintaining the balance of marine life. However, coral reefs are facing threats due to human activities and environmental issues. Coral reefs’ population is slowly decreasing day by day. It is crucial to monitor the health of coral reefs. Traditionally, researchers use manual monitoring to assess the health of coral reefs. This way is too time-consuming and not accurate. Therefore, an automatic coral reef monitoring model is proposed by using image classification and deep learning. Underwater images of coral reefs are collected from various locations. The datasets consist of 20830 images from seven different locations. The images are classified by using a deep learning method called YOLOv11. YOLOv11 consists of three main components, backbone, neck and head. Backbone extracts the features from the image using the C3k2 block. Neck combines all the extracted features. The head provides the final coral classification output and the model or class scores. Using YOLOv11, coral reef classification achieves an F1-score of up to 70%. YOLOv11 helps to detect and classify the coral reef automatically and faster compared to the manual method.