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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Yonatan Adiwinata
Department of Information Technology, Faculty of Engineering, Udayana University
Computer Science & IT

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Pengenalan Spesies Ikan dengan Faster R-CNN Inception-v2 menggunakan QUT FISH Dataset Yonatan Adiwinata; Akane Sasaoka; I Putu Agung Bayupati; Oka Sudana
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 3 (2020): Vol. 11, No. 03 December 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i03.p03

Abstract

Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.
Co-Authors Akane Sasaoka Anak Agung Kompiang Oka Sudana I Putu Agung Bayupati