FDCNet: filtering deep convolutional network for marine organism classification

Huimin Lu, Yujie Li, Tomoki Uemura, Zongyuan Ge, Xing Xu, Li He, Seiichi Serikawa, Hyoungseop Kim

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)


Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).

Original languageEnglish
Pages (from-to)21847-21860
Number of pages14
JournalMultimedia Tools and Applications
Issue number17
Publication statusPublished - 1 Sep 2018
Externally publishedYes


  • Artificial intelligence
  • Deep learning
  • Filtering deep convolutional network
  • Marine organism classification

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