Name
Title | ||
|---|---|---|
A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network |
Abstract | ||
|---|---|---|
In this paper, we are reporting a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. We used HOG and SVM in the detection part to detect the road signs captured by the camera. Then we used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. Our system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-019-04307-6 | Soft Computing |
Keywords | DocType | Volume |
Road signs, TSDR, Detection, Classification, Histogram of oriented gradients, Support-vector machine, Convolutional neural network, LeNet | Journal | 24 |
Issue | ISSN | Citations |
9 | 1432-7643 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amal Bouti | 1 | 1 | 0.35 |
| Med Adnane Mahraz | 2 | 1 | 0.35 |
| Jamal Riffi | 3 | 20 | 3.97 |
| Hamid Tairi | 4 | 57 | 17.49 |