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Detection and Recognition of U.S. Warning Signs on Curves

机译：检测和识别曲线上的美国警告信号

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Traffic sign detection and recognition has been studied in multiple areas including civil and transportation engineering, automated driving, and computer vision. However, previous work has devoted relatively less attention to U.S. signs. Among all types of U.S. signs, warning signs are the most crucial to road safety. In this work, we propose a customized detection and recognition method for U.S. warning signs that does not require training. For detection, preprocessing with a two-layer HSV-B filter and style transfer reduces color bias and a substantial number of false positives. We formulate the recognition process as a template-matching problem in which pre-trained deep networks serve as feature extractors and we use the cosine distance as the distance metric. Best results on selected images from Georgia State Route 2 achieve above 90% precision and recall in detection and 92.6% accuracy in recognition. Further tests on large datasets demonstrate that the proposed method is promising, and it can support transportation agencies in the management of their warning sign assets.
机译：交通标志的检测和识别已在多个领域进行了研究，包括土木和交通工程，自动驾驶和计算机视觉。但是，以前的工作对美国标志的关注相对较少。在所有类型的美国标志中，警告标志对道路安全至关重要。在这项工作中，我们提出了一种无需培训的针对美国警告标志的定制检测和识别方法。为了进行检测，使用两层HSV-B滤镜进行预处理并进行样式转换可以减少颜色偏差和大量的误报。我们将识别过程公式化为模板匹配问题，其中预训练的深度网络用作特征提取器，并使用余弦距离作为距离度量。从佐治亚州立2号公路选择的图像上获得的最佳结果可达到90％以上的精度和查全率，识别率达到92.6％的精度。对大型数据集的进一步测试表明，该方法是有前途的，它可以支持运输机构对其警告标志资产进行管理。

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