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Towards Condition Analysis for Machine Vision Based Traffic Sign Inventory

机译：基于机器视觉的交通标志库存状况分析

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Automatic traffic sign inventory and simultaneous condition analysis can be used to improve road maintenance processes, decrease maintenance costs, and produce up-to-date information for future intelligent driving systems. The goal of this research is to combine automatic traffic sign detection and classification with traffic sign inventory and condition analysis. This paper considers the very challenging problem of traffic sign condition analysis which is currently performed manually by experts. The manual evaluation is time-consuming, expensive, and subjective. We propose a machine vision based method to determine the condition category of each detected sign. A new dataset containing close to 400 traffic signs with condition category annotations has been specifically collected for this research since there was no suitable data available. The experimental results indicate that the average performance of the method is close to the human performance.
机译：自动交通标志库存和同时状况分析​​可用于改善道路维护流程，降低维护成本并为未来的智能驾驶系统提供最新信息。这项研究的目的是将自动交通标志检测和分类与交通标志清单和状态分析相结合。本文考虑了目前由专家手动执行的交通标志状况分析这一极具挑战性的问题。手动评估是耗时，昂贵且主观的。我们提出了一种基于机器视觉的方法来确定每个检测到的信号的状况类别。由于没有合适的数据，本研究专门收集了一个新的数据集，其中包含近400个带有条件类别注释的交通标志。实验结果表明，该方法的平均性能接近人工性能。

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