Pargol Ghavam Mostafavi - Department of Marine Sciences, Science and Research branch, Islamic Azad University Tehran, Iran
Kambiz Rahbar - Department of Computer Engineering, South Tehran Branch, Islamic Azad University Tehran, Iran
Anis Rahati - Department of Computer Engineering, South Tehran Branch, Islamic Azad University Tehran, Iran
Abbass Ghadami-Yazdi - Department of Marine Sciences, Science and Research branch, Islamic Azad University Tehran, Iran

Real-time identifying and classifying hard corals on underwater videos is a critical task to cost-effectively monitor hard corals localization. In this work, we address the problem, using darknet-YOLO framework. To this end, twenty-four convolutional layers of Darknet-YOLO is employed to detect a single hard coral class. The detection and localization method repeated on each video’s frame. To evaluated the framework performance, a collection of coral images has been extracted from the video and tagged manually. The collection consist ۱۰۰۰۰ sequential images and hard corals’ location are extracted on each frame. The system achieves approximately ۸۸.۳% on recall and ۸۸.۸% on accuracy.

coral reefs detection; coral reefs localization;darknet-YOLO; deep neural network