Citation: | ZHANG Shufang, WANG Qinyu, ZHU Tong, et al., “Detection and Classification of Small Traffic Signs Based on Cascade Network,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 719-726, 2021, doi: 10.1049/cje.2021.05.014 |
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.
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