程序使用神经网络分割光栅图像

I. Tereikovskyi, Denys Chernyshev, O. Korchenko, L. Tereikovska, O. Tereikovskyi
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摘要

目前,基于神经网络的图像语义分割方法在计算机系统中的应用越来越广泛。尽管在该领域取得了重大成功,但最重要的未解决问题之一是确定卷积神经网络的类型和参数的任务,这是编码器和解码器的基础。研究的结果是,开发了一种合适的程序,使神经网络编码器和解码器适应于分割问题的以下条件:图像大小,颜色通道数,允许的最小分割精度,允许的最大分割计算复杂度,需要标记段,需要选择几个段,需要选择变形,位移和旋转的对象,学习神经网络模型的最大计算复杂度是允许的;神经网络模型的允许训练周期。应用神经网络进行图像分割的程序实现包括基本数学支持的形成、主要模块的构建和程序的总体方案。实验结果表明,所开发的方法对包含汽车等物体的图像进行了语义分割。实验结果表明,应用该方法可以避免复杂的长期实验,建立神经网络模型,在足够短的训练周期下,保证达到0.8左右的图像分割精度,相当于同类目的的最佳系统。研究表明,在改进栅格图像的神经网络分割方法支持的方向上,进一步研究的方法应该与编码器和解码器中合理使用现代模块和机制相关联,以适应给定任务的重要条件。例如,ResNet模块的使用允许你增加神经网络的深度,因为梯度下降效果的水平,盗梦空间模块提供了加权因子数量的减少和不同大小对象的处理。
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PROCEDURE FOR USING NEURAL NETWORKS FOR SEGMENTATION OF RASTER IMAGES
Currently, means of semantic segmentation of images, based on the use of neural networks, are increasingly used in computer systems for various purposes. Despite significant successes in this field, one of the most important unsolved problems is the task of determining the type and parameters of convolutional neural networks, which are the basis of the encoder and decoder. As a result of the research, an appropriate procedure was developed that allows the neural network encoder and decoder to be adapted to the following conditions of the segmentation problem: image size, number of color channels, permissible minimum accuracy of segmentation, permissible maximum computational complexity of segmentation, the need to label segments, the need to select several segments, the need to select deformed, displaced and rotated objects, the maximum computational complexity of learning a neural network model is permissible; admissible training period of the neural network model. The implementation of the procedure of applying neural networks for image segmentation consists in the formation of the basic mathematical support, the construction of the main blocks and the general scheme of the procedure. The developed procedure was verified experimentally on examples of semantic segmentation of images containing objects such as a car. The obtained experimental results show that the application of the proposed procedure allows, avoiding complex long-term experiments, to build a neural network model that, with a sufficiently short training period, ensures the achievement of image segmentation accuracy of about 0.8, which corresponds to the best systems of a similar purpose. It is shown that the ways of further research in the direction of improving the methodological support of neural network segmentation of raster images should be correlated with the justified use of modern modules and mechanisms in the encoder and decoder, adapted to the significant conditions of the given task. For example, the use of the ResNet module allows you to increase the depth of the neural network due to the leveling of the gradient drop effect, and the Inception module provides a reduction in the number of weighting factors and the processing of objects of different sizes.
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