Name
Abstract | ||
|---|---|---|
Traffic sign detection is essential in autonomous driving. It is challenging especially when large proportion of instance to be detected are in small size. Directly applying state-of-the-art object detection algorithm Faster R-CNN for small traffic sign detection renders unsatisfactory detection rate, while a higher accuracy will be performed if the input images are upsampled. In this paper, we first investigate Faster R-CNN's network architecture, and regard its weak performance on small instances as improper receptive field. Then we augment its architecture according to receptive field with a higher accuracy achieved and no obvious incremental computational cost. Experiments are conducted to validate the effectiveness of proposed method and give an comparison to the state-of-the-art detection algorithms on both accuracy and computational cost. The experimental results demonstrate an improved detection accuracy and an competitive computing speed of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-7305-2_14 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Traffic sign detection,Convolutional Neural Network,Receptive field | Conference | 773 |
ISSN | Citations | PageRank |
1865-0929 | 2 | 0.40 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuo Zhang | 1 | 186 | 27.49 |
| Xiaolong Zhou | 2 | 2 | 0.40 |
| Sixian Chan | 3 | 12 | 7.69 |
| Sheng-Yong Chen | 4 | 1077 | 114.06 |
| Honghai Liu | 5 | 1974 | 178.69 |