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Underwater scene segmentation by deep neural network

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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.


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



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UK Robotics and Autonomous Systems, 2019


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.


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