Abstract

Among the tasks solved by artificial neural networks are the tasks of analyzing objects on the images of the underlying Earth’s surface, obtained by the on-board equipment of unmanned aerial vehicle (UAV). For the solution of such problems, the convolutional neural networks (CNN), operating semantic segmentation of the received image, are widely used. In this case, the designer of such networks has to solve the difficult task of selecting hyperparameter values for them. These values’ choice is one of the most critical tasks that have to be solved when forming a CNN. Existing attempts to solve this problem are usually based on one of two approaches. The first one involves a set of experiments with different values of hyperparameters of the CNN with learning each of the network variants. These experiments are performed until a CNN with acceptable characteristics is obtained. This approach is simple to implement but does not guarantee a CNN with high performance. The second approach treats the selection of hyperparameter values in the network as an optimization problem. If this problem is successfully solved, it is possible to obtain a CNN with sufficiently high characteristics. However, this task has a significant complexity and also requires a large consumption of computing resources. Images in the form of multidimensional arrays are used as source data to analyze objects on the underlying surface. It means that CNN will contain a significant number of parameters. Accordingly, it will take considerable time to find a suitable CNN by searching for possible hyperparameter values. This paper proposes an alternative approach to the problem of selecting the hyperparameter values of CNN based on the analysis of the processes running in the network. The effectiveness of this approach is demonstrated by solving the problem of semantic segmentation of the underlying surface obtained by remote sensing of the Earth’s surface.

中文翻译：

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解决地球表面遥感获得的图像的语义分割问题时，选择卷积神经网络的超参数值

摘要

人工神经网络解决的任务包括分析无人机表面的图像上的对象，这些图像是通过无人机（UAV）的机载设备获得的。为了解决这些问题，广泛使用了卷积神经网络（CNN），即接收图像的操作语义分割。在这种情况下，此类网络的设计者必须解决为它们选择超参数值的艰巨任务。这些值的选择是形成CNN时必须解决的最关键的任务之一。解决该问题的现有尝试通常基于两种方法之一。第一个涉及一组使用CNN超参数的不同值的实验，并学习每种网络变体。进行这些实验，直到获得具有可接受特性的CNN。这种方法易于实现，但不能保证CNN具有高性能。第二种方法将网络中超参数值的选择视为优化问题。如果成功解决了该问题，则可以获得具有足够高的特性的CNN。但是，此任务非常复杂，并且还需要大量消耗计算资源。多维数组形式的图像用作源数据，以分析底层表面上的对象。这意味着CNN将包含大量参数。因此，通过搜索可能的超参数值来寻找合适的CNN将花费大量时间。本文针对网络中运行的进程进行了分析，提出了一种选择CNN超参数值的替代方法。通过解决通过遥感地球表面获得的底层表面的语义分割问题，证明了该方法的有效性。