AUTOMATIC FISH IDENTIFICATION USING SINGLE SHOT DETECTOR

Authors

  • Arie Vatresia Universitas Bengkulu
  • Ruvita Faurina University of Bengkulu
  • Vivin Purnamasari University of Bengkulu
  • Indra Agustian University of Bengkulu

Keywords:

Fish Detection, SSD, Bengkulu, Deep Learning, Sorting Machine

Abstract

Fish identification is a way of classifying fish based on special characteristics, either through a description of the shape, body pattern of the fish, color or other characteristics. To be good handlers, the ability to identify the genus of marine fish through the help of a computer is needed so that identification can be done automatically. The deep learning method is an analytical method used to analyze large and more in-depth data. Deep learning has had the best results in the last 10 years. This study uses one of the models of deep learning, namely Single Shot Detector , a relatively simple algorithm to detect an object with the help of a mobilenet architecture. This study identified 10 genera of marine fish with a total dataset of 1000 images, with 90% training data and 10% validation data, each fish genus has 100 images with different shooting angles and backgrounds. The results showed that the Single Shot Detector model with Mobilenet architecture got an accuracy value of 52.48% for the identification of 10 genera of marine fish. Keywords: Marine Fish Identification, Single Shot Detector, Mobilenet.

Dimensions

Author Biography

Indra Agustian, University of Bengkulu

Teknik Elektro, Universitas Bengkulu

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Published

2022-06-08
Abstract 73  .