Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset

  • Yonatan Adiwinata Department of Information Technology, Faculty of Engineering, Udayana University
  • Akane Sasaoka Electrical Engineering and Computer Sciense, Kanazawa University
  • I Putu Agung Bayupati Department of Information Technology, Faculty of Engineering, Udayana University
  • Oka Sudana Department of Information Technology, Faculty of Engineering, Udayana University


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%.



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How to Cite
ADIWINATA, Yonatan et al. Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 11, n. 3, p. 144-154, dec. 2020. ISSN 2541-5832. Available at: <>. Date accessed: 27 feb. 2021. doi: