Convolutional Neural Networkを用いた海底画像からの底質判別手法

• 北川 純

  新潟大学

• 榎本 洸一郎

  新潟大学

• 戸田 真志

  熊本大学

• 三好 晃治

  地方独立行政法人北海道立総合研究機構網走水産試験場

• 桒原 康裕

  地方独立行政法人北海道立総合研究機構網走水産試験場

書誌事項

タイトル別名

• Classification Method for Bottom Sediment from Seabed Image Using Convolutional Neural Network

抄録

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.

収録刊行物

• 精密工学会誌

  精密工学会誌 83 (12), 1172-1177, 2017

  公益社団法人 精密工学会

キーワード

• scallop
• marine resources
• seabed image
• bottom sediment
• convolutional neural network

詳細情報

• CRID: 1390001204830273280
• NII論文ID: 130006243021
• DOI: 10.2493/jjspe.83.1172
• ISSN: 1882675X
• ISSN: 09120289
• Web Site: https://www.jstage.jst.go.jp/article/jjspe/83/12/83_1172/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可