Deep learning of submerged body images from 2D sonar sensor based on convolutional neural network

Given the harsh working conditions such as high-speed flow rate, turbid watch, and steep terrain, it is a very challenging task to find submerged bodies in disaster site occurred at sea or river or for the military purpose. Therefore, if it is possible to utilize the unmanned robot, such as the USV(Unmanned Surface Vehicle) and UUV (Unmanned Underwater Vehicle) for the navigational operation of these special purpose, it has a great effect. Underwater ultrasound image information is pretty difficult to make the geometric modeling of submerged body due to heavy noise on its characteristics. This study presents the robust method of submerged body recognition based on the CNN(Convolutional Neural Network), which is one of the deep learning approach.

[1]  Jie Li,et al.  Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Michael Kaess,et al.  Incremental data association for acoustic structure from motion , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.