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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yok-Yen Nguwi Kouzani, A.Z. |
| Copyright Year | 2006 |
| Description | Author affiliation: Glenayre Electron., Singapore (Yok-Yen Nguwi) |
| Abstract | An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies road signs assisting the driver of the vehicle to properly operate the vehicle. This paper presents an automatic road sign recognition system capable of analysing live images, detecting multiple road signs within images, and classifying the type of the detected road signs. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space and locates road signs. The classification module determines the type of detected road signs using a series of one to one architectural Multi Layer Perceptron neural networks. The performances of the classifiers that are trained using Resillient Backpropagation and Scaled Conjugate Gradient algorithms are compared. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 96% using Scaled Conjugate Gradient trained classifiers. |
| Starting Page | 3955 |
| Ending Page | 3962 |
| File Size | 683392 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780394909 |
| DOI | 10.1109/IJCNN.2006.246916 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-16 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Shape Lighting Image recognition Image sensors Road vehicles Vehicle driving Image segmentation Image analysis Cameras |
| Content Type | Text |
| Resource Type | Article |
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