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Underwater Live Fish Recognition by Deep Learning

  • Abdelouahid Ben Tamou
  • Abdesslam BenzinouEmail author
  • Kamal Nasreddine
  • Lahoucine Ballihi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

Recently, underwater videos have gained great interest by marine ecologists for studying fish populations. Actually, this technique produces large amount of visual data and does not affect fish behavior. However, visual processing and analyzing of the recorded data can be subjective, time consuming and costly. We propose in this paper to use the convolutional neural network AlexNet with transfer learning for automatic fish species classification. We extract features from foreground fish images of the available underwater dataset using the pretrained AlexNet network either with or without fine-tunig. For classification, we use a linear SVM classifier. The experiment results demonstrate the effectiveness of the proposed approach on the Fish Recognition Ground-Truth dataset. We achieve an accuracy of 99.45%.

Keywords

Deep learning Transfer learning Convolutional neural network Pretrained model AlexNet Fish recognition 

Notes

Acknowledgments

The authors would like to thank the Région Bretagne for financial support.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Abdelouahid Ben Tamou
    • 1
    • 2
  • Abdesslam Benzinou
    • 1
    Email author
  • Kamal Nasreddine
    • 1
  • Lahoucine Ballihi
    • 2
  1. 1.Univ Bretagne Loire, ENIB, UMR CNRS 6285 LabSTICCBrestFrance
  2. 2.LRIT-CNRST URAC 29, Mohammed V University In Rabat, FSRRabatMorocco

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