基于CNN和分布式LSTM的道路分割

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摘要翻译：
在自动驾驶系统(ADS)和高级驾驶员辅助系统(ADAS)中，有效的道路分割是感知可驾驶区域和构建路径规划占用地图的必要条件。现有的算法实现了巨大的卷积神经网络(CNNs)，计算成本高，耗时长。本文介绍了一种广泛应用于音视频处理的分布式LSTM神经网络，用于处理图像中的行、列和特征映射。然后，我们提出了一种新的结合卷积层和分布式LSTM层的网络来解决道路分割问题。最后，在KITTI路基准中对网络进行了训练和测试。结果表明，与单纯的CNN结构相比，该组合结构提高了特征提取和处理的效率，但所需的处理时间更短。
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英文标题：
《Road Segmentation Using CNN and Distributed LSTM》
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作者：
Yecheng Lyu, Lin Bai and Xinming Huang
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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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：
  In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning. The existing algorithms implement gigantic convolutional neural networks (CNNs) that are computationally expensive and time consuming. In this paper, we introduced distributed LSTM, a neural network widely used in audio and video processing, to process rows and columns in images and feature maps. We then propose a new network combining the convolutional and distributed LSTM layers to solve the road segmentation problem. In the end, the network is trained and tested in KITTI road benchmark. The result shows that the combined structure enhances the feature extraction and processing but takes less processing time than pure CNN structure.
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PDF链接：
https://arxiv.org/pdf/1808.0445

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关键词：CNN STM 分布式 Segmentation Mathematical 道路 分割 KITTI ADAS systems