Convolutional Neural Networkを用いた海底画像からの底質判別手法  [in Japanese] Classification Method for Bottom Sediment from Seabed Image Using Convolutional Neural Network  [in Japanese]

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Author(s)

Abstract

In the fisheries, understanding the fishing ground and estimating of the amount of resources are required for increasing production stability, strengthening international competitiveness and disaster recovery. Recently, The investigations of aquatic resources are carried out using DV camera. The investigations using DV camera give less damage to the fishing ground than capturing resources. Seabed videos are taken for measuring scallops in scatter scallop fishery in Hokkaido, Japan. We are able to investigate and fishery better if we get not only information of number of scallops but also bottom sediment that scallops inhabit. Our research aim is 4 types sediment classification, sand, ballast, gravel and shell beds. Shell beds are accumulation of scallop's carcasses. Using seabed images took by DV camera enable getting high precision and wide range of information. In this paper, we consider bottom sediment classification method from seabed image using Convolutional Neural Network. This experiment shows accuracy more than 95% in all sediment types.

Journal

  • Journal of the Japan Society for Precision Engineering

    Journal of the Japan Society for Precision Engineering 83(12), 1172-1177, 2017

    The Japan Society for Precision Engineering

Codes

  • NII Article ID (NAID)
    130006243021
  • Text Lang
    JPN
  • ISSN
    0912-0289
  • Data Source
    J-STAGE 
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