Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

中文翻译：

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在水下视频中使用预训练深度学习神经网络进行对象分类的有效性

使用深度学习神经网络（DLNN）进行视频图像处理和对象分类可以显着提高水下航行器的自主性。本文介绍了一个项目的结果，该项目专注于在仿生水下车辆（BUV）中实现的将DLNN用于水下视频中的对象分类（OCUV）。BUV旨在用于探测水下地雷，探索沉船或观察第二次世界大战后遗弃在海床上的弹药的腐蚀过程。在这里，预训练的DLNN用于以下类型物体的分类：鱼类，水下航行器，潜水员和障碍物。我们的研究结果使我们能够估计使用预训练的DLNN在复杂的波罗的海环境下分类不同物体的有效性。遗传算法（GA）用于建立DLNN的调整参数。比较了AlexNet的三种不同的训练方法，然后为15个网络选择了一种训练方法，并为测试提供了最终结果的描述。DLNN在带有六个中型图形处理单元（GPU）的服务器上进行了培训。最后，训练有素的DLNN在BUV板上安装的Nvidia JetsonTX2平台中实现，并且其中一个网络在真实环境中得到了验证。