Real-time Localization of Hard Corals in Underwater Videos Using Darknet-YOLO Network
Publish place: 4th National Conference on Computer, Information Technology and Applications of Artificial Intelligence
Publish Year: 1399
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CEITCONF04_023
تاریخ نمایه سازی: 13 تیر 1400
Abstract:
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.
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Authors
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