Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

中文翻译：

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使用深度学习进行图像分割：一项调查

图像分割是图像处理和计算机视觉中的一个关键主题，其应用包括场景理解、医学图像分析、机器人感知、视频监控、增强现实和图像压缩等。文献中已经开发了各种用于图像分割的算法。最近，由于深度学习模型在广泛的视觉应用中取得成功，已经有大量工作旨在开发使用深度学习模型的图像分割方法。在本次调查中，我们对撰写本文时的文献进行了全面回顾，涵盖了语义和实例级分割的广泛开创性工作，包括全卷积像素标记网络、编码器-解码器架构、多尺度和基于金字塔的方法，对抗环境中的循环网络、视觉注意模型和生成模型。我们调查这些深度学习模型的相似性、优势和挑战，检查最广泛使用的数据集，报告性能，并讨论该领域有前途的未来研究方向。