UKRAS 2019-Yang.pdf (422.98 kB)
Underwater scene segmentation by deep neural network
conference contribution
posted on 2019-03-18, 14:48 authored by Yang Zhou, Jiangtao Wang, Baihua LiBaihua Li, Qinggang MengQinggang Meng, Emanuele Rocco, Andrea SaianiA 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, 2019Citation
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
2019Notes
This is a conference paper.Language
- en