Speed sign is one of the most important instant indications for drivers to adjust the speed of their cars. In the literature, almost all of the existing methods for speed sign recognition are based on static pictures with clean images. When dealing with the traffic signs in complex environments, these existing approaches often have inaccurate detected areas, which lead to the difficulty of recognition. The authors propose a deep cascade network to improve the recognition of the speed signs with a structure of cascade subnetworks. The proposed network is composed of a localisation subnetwork and a classification subnetwork. The difficult issue in complex scenarios is the detection of the speed sign due to its small resolution, occlusion, colour fading etc. The proposed localisation subnetwork can improve the localisation accuracy by borrowing the idea of locating the targets from coarse to fine. Ultimately, the classification sub-network extracts more effective features for speed sign recognition. The experimental results illustrate that the proposed method outperforms the YOLOv2 or YOLOv3 model in identifying the speed sign in complex scenarios with at least 6% higher in terms of area under curve, and this will promote the improvement of recognition significantly.

中文翻译：

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基于深度级联网络的复杂场景中的速度标志识别

速度标志是驾驶员调整汽车速度最重要的即时指示之一。在文献中，几乎所有现有的速度标志识别方法都是基于具有清晰图像的静态图片。当在复杂环境中处理交通标志时，这些现有方法通常具有不正确的检测区域，这导致识别困难。作者提出了一种深层级联网络，以通过级联子网的结构来提高对速度标志的识别。所提出的网络由定位子网和分类子网组成。在复杂的场景中，困难的问题是由于速度符号的分辨率小，遮挡，褪色等原因导致的速度符号检测。所提出的定位子网可以通过从粗到细定位目标的想法来提高定位精度。最终，分类子网提取出更有效的特征以进行速度标志识别。实验结果表明，该方法在识别复杂场景下的速度符号方面优于YOLOv2或YOLOv3模型，其曲线下面积至少高6％，这将显着促进识别能力的提高。