چکیده مقاله
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 10000 sequential images and hard corals’ location are extracted on each frame The system achieves approximately 88 3% on recall and 88 8% on accuracy
کلیدواژهها
نویسندگان
Pargol Ghavam Mostafavi
Department of Marine Sciences, Science and Research branch, Islamic Azad University Tehran, Iran
کامبیز رهبر
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
شیوه ارجاع
Ghavam Mostafavi, Pargol and Rahbar, Kambiz and Rahati, Anis and Ghadami-Yazdi, Abbass,1399,Real-time Localization of Hard Corals in Underwater Videos Using Darknet-YOLO Network,4th National Conference on Computer, Information Technology and Applications of Artificial Intelligence,Ahvaz
ارائهشده در
مجموعه مقالات چهارمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی15 بهمن 1399 · اهواز