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Survey on semantic segmentation using deep learning techniques

机译：深层学习技术对语义分割的调查

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摘要

Semantic segmentation is a challenging task in computer vision systems. A lot of methods have been developed to tackle this problem ranging from autonomous vehicles, human-computer interaction, to robotics, medical research, agriculture and so on. Many of these methods have been built using the deep learning paradigm that has shown a salient performance. For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. Second, by providing an overview of the publicly available datasets on which they have been assessed. In addition, we present the common evaluation matrix used to measure their accuracy. Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances. Finally, we conclude by discussing some of the open problems and their possible solutions. (C) 2019 Elsevier B.V. All rights reserved.
机译：语义分割是计算机视觉系统中有挑战性的任务。已经开发了许多方法来解决这个问题，从自主车辆，人机互动，机器人，医学研究，农业等等。许多这些方法已经使用了已经显示出显着性能的深度学习范例。出于这个原因，我们建议根据其架构的常见概念首先将它们分为十个不同的类别。其次，通过提供他们被评估的公开数据集的概述。此外，我们介绍了用于测量其准确性的常见评估矩阵。此外，我们专注于一些方法，并仔细观察其架构，以了解他们如何达到报告的表现。最后，我们通过讨论一些公开问题及其可能的解决方案来结束。 （c）2019 Elsevier B.v.保留所有权利。

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