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Traffic Sign Recognition

This is my first project about training a deep learning algorithm for road traffic signs recognition and was mostly inspired by the Traffic Signs detection competition in Kaggle : https://www.kaggle.com/c/traffic-sign-recognition/overview

plot

For this project, I used the German Traffic Sign dataset from the Institut Fur Neuroinformatik : https://benchmark.ini.rub.de/

J. Stallkamp, M. Schlipsing, J. Salmen and C. Igel, "The German Traffic Sign Recognition Benchmark: A multi-class classification competition," The 2011 International Joint Conference on Neural Networks, San Jose, CA, 2011, pp. 1453-1460, doi: 10.1109/IJCNN.2011.6033395.

Notebooks

For this work, you can find utilies functions in this file and the notebooks are commented steps by steps from preprocessing data to model architecture and training the model, then predictions on new images.

  1. The Road_Signs_Detection_Model notebook is using for preprocessing data, defining model architecture and train the model.
  2. The Testing_Notebook-RSD_Model as its name suggests, is using for testing the model on test sets and never-seen-before images.

Dataset structure

This dataset consists in 43 classes of more than 50 000 images using for training, validation and test, it can be found here. The labels are stored in the signnames.csv file.

The dataset are divided as follow :

  • 34799 images for the training set.
  • 4410 images for the validation set.
  • 12630 images for the test set.

The images' shapes are (32, 32, 3) (RGB).

The training set archive is structured as follows :

  • One directoy per class
  • Each directory contains one Comma Separated Value (CSV) file with annotations (GT-ClassID.csv), as well as the training images.
  • Training images are grouped by tracks.
  • Each tracks contains 30 images of one single physical traffic sign.

Image format

The image format is structured as follows:

  • The images contain one traffic sign each.
  • Images contain a border of 10% around the actual traffic sign (cropped to at least 5 pixels) to allow for edge-based approaches.
  • They are stored in Pickle5 format (PPM format).
  • Images sizes vary from 15x15 to 250x250 and aren't necessarily squared.
  • Some of the traffic sign are not necessarily centered within the image. This is only valid for images that were close to the image border in the before-cropped image.

The 43 different classes are :

  • 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