Underwater Scene Segmentation by Deep Neural Network
Yang Zhou, Jiangtao Wang, Baihua Li, Qinggang Meng, Emanuele Rocco, Witted Srl, Andrea Saiani, Witted Srl · Journal of robotics & autonomous systems · 2019
A deep neural network architecture is proposed in this paper for underwater scene semantic segmentation. The architecture consists of encoder and decoder networks. Pre- trained 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.