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Performance evaluation of road detection and following systems

机译：道路检测和跟踪系统的性能评估

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We describe a methodology for evaluating algorithms to provide quantitative information about how well road detection and road following algorithms perform. The approach relies on generating a set of standard data sets annotated with ground truth. We evaluate the algorithms used to detect roads by comparing the output of the algorithms with ground truth, which we obtain by having humans annotate the data sets used to test the algorithms. Ground truth annotations are acquired from more than one person to reduce systematic errors. Results are quantified by looking at false positive and false negative regions of the image sequences when compared with the ground truth. We describe the evaluation of a number of variants of a road detection system based on neural networks.
机译：我们描述了一种评估算法的方法，以提供有关道路检测和道路跟踪算法性能的定量信息。该方法依赖于生成一组标有地面事实的标准数据集。我们通过将算法的输出与地面真相进行比较来评估用于检测道路的算法，这是通过让人类注释用于测试算法的数据集而获得的。从多个人那里获得了地面事实注释，以减少系统性错误。与地面实况比较时，通过查看图像序列的假阳性和假阴性区域来量化结果。我们描述了基于神经网络的道路检测系统的多种变型的评估。

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