UKRAS 2019-Yang.pdf (422.98 kB)
Download file

Underwater scene segmentation by deep neural network

Download (422.98 kB)
conference contribution
posted on 18.03.2019, 14:48 by Yang ZhouYang Zhou, Jiangtao Wang, Baihua LiBaihua Li, Qinggang MengQinggang Meng, Emanuele Rocco, Andrea Saiani
A deep neural network architecture is proposed in this paper for underwater scene semantic segmentation. The architecture consists of encoder and decoder networks. Pretrained VGG-16 network is used as a feature extractor, while the decoder learns to expand the lower resolution feature maps. The network applies max un-pooling operator to avoid large number of learnable parameters, and, in order to make use of the feature maps in encoder network, it concatenates the feature maps with decoder and encoder for lower resolution feature maps. Our architecture shows capabilities of faster convergence and better accuracy. To get a clear view of underwater scene, an underwater enhancement neural network architecture is described in this paper and applied for training. It speeds up the training process and convergence rate in training.

Funding

The authors are grateful to the EPSRC Centre for Doctoral Training in Embedded Intelligence under grant reference EP/L014998/1 for financial support sponsored by Witted Srl, Italy

History

School

  • Science

Department

  • Computer Science

Published in

UK Robotics and Autonomous Systems, 2019

Citation

ZHOU, Y. ... et al., 2019. Underwater scene segmentation by deep neural network.Presented at the 2nd UK Robotics and Autonomous Systems Conference, (UK-RAS 2019), Loughborough University, 24th January.

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2019

Notes

This is a conference paper.

Language

en

Location

UK

Usage metrics

Keywords

Exports