论文标题
一项关于实时语义图像分割深度学习方法的调查
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time
论文作者
论文摘要
语义图像分割是计算机视觉中增长最快的区域之一，并具有多种应用。在许多领域，例如机器人技术和自动驾驶汽车，语义图像分割至关重要，因为它为基于像素级别的场景理解而采取的行动提供了必要的背景。此外，医学诊断和治疗的成功取决于对所考虑的数据和语义图像分割的极为准确的理解，这在许多情况下是重要的工具之一。深度学习的最新发展提供了许多工具来有效地解决这一问题，并提高了准确性。这项工作提供了对图像分割中最先进的深度学习体系结构的全面分析，更重要的是，列出了广泛的技术，以实现快速的推理和计算效率。这些技术的起源及其优势和权衡是通过对其对该地区的影响进行深入分析的讨论。总结最佳的体系结构，并使用用于实现这些最先进结果的方法列表。
Semantic image segmentation is one of fastest growing areas in computer vision with a variety of applications. In many areas, such as robotics and autonomous vehicles, semantic image segmentation is crucial, since it provides the necessary context for actions to be taken based on a scene understanding at the pixel level. Moreover, the success of medical diagnosis and treatment relies on the extremely accurate understanding of the data under consideration and semantic image segmentation is one of the important tools in many cases. Recent developments in deep learning have provided a host of tools to tackle this problem efficiently and with increased accuracy. This work provides a comprehensive analysis of state-of-the-art deep learning architectures in image segmentation and, more importantly, an extensive list of techniques to achieve fast inference and computational efficiency. The origins of these techniques as well as their strengths and trade-offs are discussed with an in-depth analysis of their impact in the area. The best-performing architectures are summarized with a list of methods used to achieve these state-of-the-art results.
