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Semantic segmentation using reinforced fully convolutional densenet with multiscale kernel

  • Sourour Brahimi
  • Najib Ben AounEmail author
  • Alexandre Benoit
  • Patrick Lambert
  • Chokri Ben Amar
Article
  • 31 Downloads

Abstract

In recent years, semantic segmentation has become one of the most active tasks of the computer vision field. Its goal is to group image pixels into semantically meaningful regions. Deep learning methods, in particular those who use convolutional neural network (CNN), have shown a big success for the semantic segmentation task. In this paper, we will introduce a semantic segmentation system using a reinforced fully convolutional densenet with multiscale kernel prediction method. Our main contribution is to build an encoder-decoder based architecture where we increase the width of dense block in the encoder part by conducting recurrent connections inside the dense block. The resulting network structure is called wider dense block where each dense block takes not only the output of the previous layer but also the initial input of the dense block. These recurrent structure emulates the human brain system and helps to strengthen the extraction of the target features. As a result, our network becomes deeper and wider with no additional parameters used because of weights sharing. Moreover, a multiscale convolutional layer has been conducted after the last dense block of the decoder part to perform model averaging over different spatial scales and to provide a more flexible method. This proposed method has been evaluated on two semantic segmentation benchmarks: CamVid and Cityscapes. Our method outperforms many recent works from the state of the art.

Keywords

Semantic Segmentation Fully Convolutional DenseNet Wider Dense Block MultiScale kernel prediction 

Notes

Acknowledgements

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48. LISTIC experiments have been made possible thanks to the MUST computing center of the University of Savoie Mont Blanc.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Sourour Brahimi
    • 1
  • Najib Ben Aoun
    • 1
    • 2
    Email author
  • Alexandre Benoit
    • 3
  • Patrick Lambert
    • 3
  • Chokri Ben Amar
    • 1
    • 4
  1. 1.REGIM-Lab.: REsearch Groups in Intelligent MachinesUniversity of Sfax, National Engineering School of Sfax (ENIS)SfaxTunisia
  2. 2.Department of Computer Science, College of Computer Science and Information TechnologyAl-Baha UniversityAl BahaSaudi Arabia
  3. 3.LISTIC-Lab: Univ. Savoie Mont Blanc, LISTICPolytech Annecy ChambéryAnnecyFrance
  4. 4.Department of Computer Engineering, College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

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