Traffic Sign Recognition and Classification

This project was created as the final year B.Tech project

In this project, we implemented a recognition and a classification model. The recognition system was used the darknet framework and the YOLO v3 model to detect traffic signs in a frame of a video. Whereas, the classification system was based on convolutional neural network (CNN). The YOLO model determined where the traffic sign was located in the frame and the CNN model determined the class of that traffic sign. We used a dataset with 21 classes of Traffic signs.

1. Speed Limit - 20
2. Speed Limit - 30
3. No Entry
4. Right Turn Prohibited
5. Left Turn Prohibited
6. Horn Prohibited
7. No Parking
8. No Stopping or Standing
9. Compulsory Ahead
10. Compulsory Left Ahead
11. Straight or Right
12. Speed Limit: 50
13. Compulsory Right Ahead
14. Speed Limit: 60
15. Speed Limit: 70
16. Speed Limit: 90
17. Speed Limit: 100
18. Speed Limit: 120
19. Stop
20. Give Way

The results generated from the model can be viewed here.