Name
Abstract | ||
|---|---|---|
This paper focuses on traffic sign spotting (TSS which automatically recognizes not only the conventional traffic signs but also information, facility and service signs, and traffic lights. TSS is divided into two sequential tasks: detecting traffic sign candidate regions in an image and recognizing the traffic signs in the regions. It is a very challenging task. We make the following contributions: 1) we create a traffic sign collection from the driverless car. The traffic signs are shot under the natural environment which covers large variation in illuminance and weather conditions. It not only contains the common traffic signs but also contains the information, facility and service signs which are called signposts, as well as traffic lights. 2) we proposed a systematic solution. We construct an Inception convolutional neural network. We use Faster-RCNN for traffic sign detection and make it suitable to detect small targets. 3) We adopt three schemes for the common traffic signs, the signposts and the traffic lights, respectively. The experimental results demonstrate the effectiveness and efficiency of our methods. Our methods won the first place in the traffic sign recognition task of Intelligent Vehicle Future Challenge 2017, China. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IVS.2018.8500650 | 2018 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | Field | DocType |
traffic sign,detection,recognition,Inception convolutional neural networks | Kernel (linear algebra),Computer vision,Object detection,Task analysis,Convolutional neural network,Computer science,Traffic sign recognition,Artificial intelligence,Traffic sign,Spotting,Traffic sign detection | Conference |
ISSN | ISBN | Citations |
1931-0587 | 978-1-5386-4453-9 | 1 |
PageRank | References | Authors |
0.39 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinkang Guo | 1 | 2 | 0.73 |
| Jianyun Lu | 2 | 1 | 0.39 |
| Yanyun Qu | 3 | 216 | 38.66 |
| Cui-Hua Li | 4 | 74 | 13.24 |