基于神经网络的道路分割

Tsz Hong Lau
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引用次数: 0

摘要

自动驾驶是汽车行业下一个最大的技术进步。然而,目前的技术在很大程度上仍处于起步阶段。摄像头和激光雷达系统等传感器网络用于记录和测量路况。而神经网络则被用来理解道路状况,并做出正确的驾驶决策。在本文中,我们特别关注自动驾驶汽车技术的道路分割。我们将介绍Oliveira等人[5]和Caltagirone等人[2]的两种道路分割方法,我们将比较每种方法在称为KITTI数据集的道路基准数据集上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Road Segmentation with Neural Networks
Autonomous driving is the next biggest technological ad-vance in the automobile industry. However, the current technology is still very much in its infancy. Networks of sensors such as cameras and LIDAR systems are used to record and measure the road condition. While neural networks are used to understand the road condition and make the correct de-cision to drive the vehicle. In this paper, we are specifically focusing on the road segmentation of autonomous vehicle technology. We will be going over the two approaches to road segmentation by Oliveira, et al [5] and Caltagirone, et al [2], and we will compare the performance of each approach on a road benchmark dataset called KITTI dataset.
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