Fine-grained visual marine vessel classification for coastal surveillance and defense applications

Berkan Solmaz, Erhan Gundogdu, Kaan Karaman, Veysel Yücesoy, Aykut Koç

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

Original languageEnglish (US)
Title of host publicationElectro-Optical Remote Sensing XI
EditorsOve Steinvall, Gary Kamerman
PublisherSPIE
ISBN (Electronic)9781510613324
DOIs
StatePublished - 2017
Externally publishedYes
EventElectro-Optical Remote Sensing XI 2017 - Warsaw, Poland
Duration: Sep 11 2017Sep 12 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10434
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceElectro-Optical Remote Sensing XI 2017
Country/TerritoryPoland
CityWarsaw
Period9/11/179/12/17

Keywords

  • coastal surveillance
  • convolutional neural networks
  • deep learning
  • fine-grained visual categorization
  • image categorization
  • image verification
  • naval marine vessels

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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