Name
Abstract | ||
|---|---|---|
In this paper we propose a real-time traffic sign recognition algorithm which is robust to the small-sized objects and can identify all traffic sign categories. Specifically, we present a two-level detection framework which consists of the region proposal module(RPM) which is responsible for locating the objects and the classification module(CM) which aims to classify the located objects. In addition, to solve the problem of insufficient samples, we present an effective data augmentation method based on traffic sign logo to generate enough training data. The experiments are conducted in TT100k, and the results show the superiority of our method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-020-08722-y | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Traffic sign recognition, Small object detection, Data augmentation, Image synthesis | Journal | 79 |
Issue | ISSN | Citations |
25 | 1380-7501 | 2 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiqiang Wu | 1 | 4 | 2.47 |
| Zhiyong Li | 2 | 64 | 11.15 |
| Ying Chen | 3 | 2 | 0.37 |
| Ke Nai | 4 | 12 | 2.90 |
| Jin Yuan | 5 | 18 | 3.65 |