Name
Abstract | ||
|---|---|---|
In this paper, we present a new approach to detect traffic signs based on cascaded convolutional neural networks (CNNs). First, the local binary pattern (LBP) feature detector and the AdaBoost classifier are combined to extract regions of interest (ROI) for coarse selection. Next, cascaded CNNs are employed to reduce negative samples of ROI for traffic sign recognition. Compared with the conventional CNN, our CNN contains three convolutional layers and its classification part is replaced by the support vector machine (SVM). The German traffic sign detection benchmark is used and experimental results demonstrate that the proposed method can achieve competitive results when compared with the state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SNPD.2016.7515901 | 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) |
Keywords | Field | DocType |
traffic sign detection,cascaded convolutional neural networks,support vector machine,local binary pattern,AdaBoost | AdaBoost,Pattern recognition,Convolutional neural network,Computer science,Convolution,Local binary patterns,Support vector machine,Feature extraction,Traffic sign recognition,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-0804-9 | 7 | 0.47 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Zang | 1 | 98 | 12.40 |
| Junqi Zhang | 2 | 72 | 5.41 |
| Dongdong Zhang | 3 | 21 | 1.77 |
| Maomao Bao | 4 | 7 | 0.47 |
| Jiujun Cheng | 5 | 166 | 10.39 |
| Keshuang Tang | 6 | 7 | 1.48 |