View Demo · Report Bug · Request Feature

Classify Traffic Signs Using Deep Learning for Self-Driving Cars

About The Project

In this hands-on project, I will train deep learning models known as Convolutional Neural Networks (CNNs) to classify 43 traffic sign images. This project could be practically applied to self-driving cars. In this hands-on project I will go through the following tasks:

• (1) Import libraries and datasets
• (2) Images visualization
• (3) Convert images to gray-scale and perform normalization
• (4) Build deep learning model
• (5) Compile and train deep learning model
• (6) Assess trained model performance

Dataset

I want to classify images of traffic signs using deep Convolutional Neural Networks (CNNs).

• The dataset consists of 43 different classes of images.
• Classes are as listed below:
  • 0 = Speed limit (20km/h)
  • 1 = Speed limit (30km/h)
  • 2 = Speed limit (50km/h)
  • 3 = Speed limit (60km/h)
  • 4 = Speed limit (70km/h)
  • 5 = Speed limit (80km/h)
  • 6 = End of speed limit (80km/h)
  • 7 = Speed limit (100km/h)
  • 8 = Speed limit (120km/h)
  • 9 = No passing
  • 10 = No passing for vehicles over 3.5 metric tons
  • 11 = Right-of-way at the next intersection
  • 12 = Priority road
  • 13 = Yield
  • 14 = Stop
  • 15 = No vehicles
  • 16 = Vehicles over 3.5 metric tons prohibited
  • 17 = No entry
  • 18 = General caution
  • 19 = Dangerous curve to the left
  • 20 = Dangerous curve to the right
  • 21 = Double curve
  • 22 = Bumpy road
  • 23 = Slippery road
  • 24 = Road narrows on the right
  • 25 = Road work
  • 26 = Traffic signals
  • 27 = Pedestrians
  • 28 = Children crossing
  • 29 = Bicycles crossing
  • 30 = Beware of ice/snow
  • 31 = Wild animals crossing
  • 32 = End of all speed and passing limits
  • 33 = Turn right ahead
  • 34 = Turn left ahead
  • 35 = Ahead only
  • 36 = Go straight or right
  • 37 = Go straight or left
  • 38 = Keep right
  • 39 = Keep left
  • 40 = Roundabout mandatory
  • 41 = End of no passing
  • 42 = End of no passing by vehicles over 3.5 metric tons

(back to top)

Installation

1. Clone the repo

   https://github.com/nqkhanh2002/Classify-Traffic-Signs-Using-Deep-Learning-for-Self-Driving-Cars.git

2. Run the jupyter notebook Notebook will automatically download data to your device. During notebook execution, use the package installer for Python to install packages that you are missing.

Contact

• Facebook : Nguyễn Quốc Khánh
• Email : nqkdeveloper@gmail.com

Citation

J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460. 2011. @inproceedings{Stallkamp-IJCNN-2011, author = {Johannes Stallkamp and Marc Schlipsing and Jan Salmen and Christian Igel}, booktitle = {IEEE International Joint Conference on Neural Networks}, title = {The {G}erman {T}raffic {S}ign {R}ecognition {B}enchmark: A multi-class classification competition}, year = {2011}, pages = {1453--1460} }

(back to top)