A Review on Deep Learning Techniques Applied to Semantic Segmentation-猫眼课题宝

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发表时间：

2017-04

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ArXiv

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其他

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作者：

Alberto Garcia-Garcia-Alberto-Garcia-Garcia-1397392435;Sergio Orts;Sergiu Oprea;Victor Villena-Martinez;J. G. Rodríguez

图像语义分割越来越受到计算机视觉和机器学习研究者的关注。许多正在崛起的应用程序需要准确而高效的分割机制：自动驾驶、室内导航，甚至虚拟或增强现实系统等等。这一需求与深度学习方法在几乎所有与计算机视觉相关的领域或应用目标中的兴起不谋而合，包括语义分割或场景理解。本文综述了语义切分的深度学习方法在不同应用领域的应用。首先，我们描述了该领域的术语以及强制性的背景概念。接下来，主要的数据集和挑战被暴露出来，以帮助研究人员决定哪些是最适合他们的需求和目标的。然后，回顾了现有的方法，强调了它们的贡献和在该领域的重要性。最后，给出了所描述方法的定量结果以及对它们进行评估的数据集，并对结果进行了讨论。最后，我们指出了一系列有前景的工作，并对基于深度学习技术的语义切分的研究现状做出了自己的结论。

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.

A Review on Deep Learning Techniques Applied to Semantic Segmentation

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