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摘要翻译：
深度学习是一种快速发展的机器学习方法，用于感知和理解大量数据。本文介绍了近年来在机器学习领域备受关注的深度学习方法，并在语义图像分割方面进行了应用，以帮助自动驾驶汽车。该应用程序使用完全卷积网络(FCN)体系结构来实现，该体系结构是通过修改基于深度学习的卷积神经网络(CNN)体系结构而获得的。实验研究了FCN-AlexNet、FCN-8s、FCN-16s和FCN-32S四种不同的FCN体系结构。在实验研究中，首先分别对FCNs进行训练，并比较这些训练后的网络模型在所用数据集上的验证精度。此外，图像分割推理是可视化的，以考虑如何精确的FCN体系结构可以分割对象。
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英文标题：
《A Brief Survey and an Application of Semantic Image Segmentation for
Autonomous Driving》
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作者：
\c{C}a\u{g}r{\i} Kaymak and Ay\c{s}eg\"ul U\c{c}ar
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Deep learning is a fast-growing machine learning approach to perceive and understand large amounts of data. In this paper, general information about the deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolutional Network (FCN) architectures obtained by modifying the Convolutional Neural Network (CNN) architectures based on deep learning. Experimental studies for the application are utilized 4 different FCN architectures named FCN-AlexNet,FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.
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PDF链接：
https://arxiv.org/pdf/1808.08413
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