Name
Abstract | ||
|---|---|---|
Recently, deep learning has greatly promoted the performance of license plate recognition (LPR) by learning robust features from numerous labeled data. However, the large variation of wild license plates across complicated environments and perspectives is still a huge challenge to the robust LPR. To solve the problem, we propose an effective and efficient shared adversarial training network (SATN)... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2019.2961744 | IEEE Access |
Keywords | Field | DocType |
Licenses,Training,Semantics,Feature extraction,Generative adversarial networks,Standards,Distortion | Computer science,Artificial intelligence,Labeled data,Deep learning,Machine learning,Distributed computing,License,Adversarial system | Journal |
Volume | ISSN | Citations |
8 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Zhang | 1 | 0 | 0.34 |
| Guozhi Tang | 2 | 0 | 0.34 |
| Yuliang Liu | 3 | 66 | 13.22 |
| Huiyun Mao | 4 | 23 | 3.26 |