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A Traffic Sign Text Detection System for Pratical Natural Scenes

机译：实际自然场景的交通标志文本检测系统

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Up to now, object detection capabilities have recently made extraordinary leaps, thanks to the application of deep learning in the field of computer vision. Currently, most of the object detection algorithms are variations of RPN (Region Proposal Network), and have basically met the requirements of industrial applications. However, in the specific business deployment, there will be many problems, such as more complex actual images, large number of small objects, poor-quality images and so on. To solve these problems, some well-designed operations need to be implemented before the algorithm can be deployed successfully. Aiming at many problems in the actual deployment of traffic sign text detection, this paper proposed a multi-stage detection pipeline, which solves many problems of traffic sign text detection in the actual road conditions. Finally, the AP (Average Precision) in our own test set is 0.9918 (IOU=0.5), which meets the practical requirements.
机译：到目前为止，由于在计算机视野领域的应用，最近对物体检测能力最近取得了非凡的跨利赛。目前，大多数对象检测算法是RPN（区域提议网络）的变体，并且基本满足了工业应用的要求。但是，在特定的业务部署中，会有很多问题，如更复杂的实际图像，大量的小物体，劣质图像等。为了解决这些问题，在算法可以成功部署之前需要实现一些精心设计的操作。针对许多问题在实际部署交通标志文本检测中，提出了一种多级检测管道，其解决了实际道路状况中交通标志文本检测的许多问题。最后，我们自己的测试集中的AP（平均精度）为0.9918（iou = 0.5），这符合实际要求。

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