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

机译:基于卷积神经网络的2D声纳传感器深度学习

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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.
机译:鉴于苛刻的工作条件,如高速流量,浑浊手表和陡峭地形,这是一个非常具有挑战性的任务,在海上或河流或军事目的中发现灾害遗址中的淹没机构是非常具有挑战性的。因此,如果可以利用无人机机器人,例如USV(无人面的表面车辆)和UUV(无人驾驶水下车辆),用于这些特殊用途的导航操作,它具有很大的效果。由于其特征严重噪音,水下超声图像信息非常难以使浸没体的几何建模。本研究介绍了基于CNN(卷积神经网络)的淹没体识别的鲁棒方法,这是一种深度学习方法之一。

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