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Robust detection, classification and positioning of traffic signs from street-level panoramic images for inventory purposes

机译:出于清单目的,从街道级全景图像中对交通标志进行可靠的检测,分类和定位

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Accurate inventories of traffic signs are required for road maintenance and increase of the road safety. These inventories can be performed efficiently based on street-level panoramic images. However, this is a challenging problem, as these images are captured under a wide range of weather conditions. Besides this, occlusions and sign deformations occur and many sign look-a-like objects exist. Our approach is based on detecting present signs in panoramic images, both to derive a classification code and to combine multiple detections into an accurate position of the signs. It starts with detecting the present signs in each panoramic image. Then, all detections are classified to obtain the specific sign type, where also false detections are identified. Afterwards, detections from multiple images are combined to calculate the sign positions. The performance of this approach is extensively evaluated in a large, geographical region, where over 85% of the 3; 341 signs are automatically localized, with only 3:2% false detections. As nearly all missed signs are detected in at least a single image, only very limited manual interactions have to be supplied to safeguard the performance for highly accurate inventories.
机译:道路维护和提高道路安全需要准确的交通标志清单。可以基于街道级全景图像有效地执行这些清单。但是,这是一个具有挑战性的问题,因为这些图像是在各种天气条件下捕获的。除此之外,还会发生遮挡和符号变形,并且存在许多符号相似的对象。我们的方法基于检测全景图像中的当前标志,以导出分类代码并将多个检测结果组合到标志的准确位置中。首先检测每个全景图像中的当前标志。然后,将所有检测分类,以获得特定的符号类型,并在此还标识错误检测。然后,将来自多个图像的检测结果组合起来以计算符号位置。在一个较大的地理区域中,对该方法的性能进行了广泛的评估,在这三个区域中,超过85%的区域; 341个符号自动定位,只有3:2%的错误检测。由于至少在单个图像中几乎检测到所有遗漏的迹象,因此仅需提供非常有限的手动交互操作即可维护高度准确的清单的性能。

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