Detection and Classification of Small Traffic Signs Based on Cascade Network

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The Institution of Engineering and Technology


Research on the traffic sign detection is significant for driverless technology, which provides useful navigation information. Existing object detection methods are only applicable to large-size objects or small-scale specific types of traffic signs, and the performance of detecting traffic signs in street views is not adequate. In this regard, we propose a method to detect and classify small traffic signs by constructing a cascaded network. Specifically, the RetinaNet network is adopted firstly to integrate multi-layer information to identify small traffic signs in traffic scene images. The focal loss function is used to balance the biased distribution of traffic sign categories. Then, a two-class network is cascaded after the RetinaNet, which helps identify valid traffic signs from the first-stage prediction results. Experiments show that our cascaded network structure could achieve the balance of different categories of predictions and an improvement in precision and recall.