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German Traffic Sign Recognition

In this project, I've built and trained a deep neural network to classify German traffic signs using Tensorflow.

sample_images

Getting Started

You can set up the required python library as follows:

conda env create -f environments-gpu.yml  # with GPU

If you have no CUDA-enabled GPU, you can use the following:

conda env create -f environments.yml  # with CPU

You can start the notebook as follows:

jupyter notebook German_Traffic_Sign_Classifier.ipynb

Train and Test Data

I have used German Traffic Sign Dataset where the bounding box locations and labels for traffic signs are provided. Here is a random sample of images:

Download the dataset. This is a pickled dataset in which we've already resized the images to 32x32.

  • It contains three pickle (.p) files:

    • train.p: The training set.
    • test.p: The testing set.
    • valid.p: The validation set.

We will use Python pickle to load the data.

Data Set Summary & Exploration

I used the pandas library to calculate summary statistics of the traffic signs data set:

  • The size of training set is 35209
  • The size of the validation set is 4000
  • The size of test set is 12630
  • The shape of a traffic sign image is (32, 32, 3)
  • The number of unique classes/labels in the data set is 43

Here is an exploratory visualization of the data set. It is a bar chart showing how the data is ditributed across the different labels.

Data Preprocessing

In this step, we will apply several preprocessing steps to the input images to achieve the best possible results.

We will use the following preprocessing techniques:

  1. Shuffling: In general, we shuffle the training data to increase randomness and variety in training dataset, in order for the model to be more stable. We will use sklearn to shuffle our data.

  2. Grayscaling: In their paper "Traffic Sign Recognition with Multi-Scale Convolutional Networks" published in 2011, P. Sermanet and Y. LeCun stated that using grayscale images instead of color improves the ConvNet's accuracy. We will use OpenCV to convert the training images into grey scale.

  3. Normalization: Normalization is a process that changes the range of pixel intensity values. Usually the image data should be normalized so that the data has mean zero and equal variance.

  4. Local Histogram Equalization: This technique simply spreads out the most frequent intensity values in an image, resulting in enhancing images with low contrast. Applying this technique will be very helpfull in our case since the dataset in hand has real world images, and many of them has low contrast. We will use skimage to apply local histogram equalization to the training images.

Original Images

Preprocessed/Transformed Images

Model Architecture and Results

LeNet

The model is based on LeNet by Yann LeCun. It is a convolutional neural network designed to recognize visual patterns directly from pixel images with minimal preprocessing. It can handle hand-written characters very well.

Source: http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

Our model is adapted from the LeNet as follows.

  • The inputs are 32x32 (RGB - 3 channels) images
  • The activation function is ReLU except for the output layer which uses Softmax
  • The output has 43 classes
Layer Shape
Input 32x32x3
Convolution (valid, 5x5x6) 28x28x6
Max Pooling (valid, 2x2) 14x14x6
Activation (ReLU) 14x14x6
Convolution (valid, 5x5x16) 10x10x16
Max Pooling (valid, 2x2) 5x5x16
Activation (ReLU) 5x5x16
Flatten 400
Dense 120
Activation (ReLU) 120
Dense 84
Activation (ReLU) 84
Dense 43
Activation (Softmax) 43

Results

Learning curve

Confusion Matrix

Test a Model on New Images

Here are ten German traffic signs that I found on the web:

Since these images are not in the right shape accepted by the classifier they were downsampled and smoothed before applying the preprocess_dataset function.

Below I visualize the softmax probablities for each test image :

Conclusion

This pipeline was very effective during the experimentations. In the end, the traffic sign classifier works pretty well overall with the test set.

However, the network did not work as good with ramdom sample images from the internet.

Moreover, if a self-driving car needs to find traffic signs in public, it first needs to know where the traffic signs are. For speeding signs, we may need an OCR (object recognition) mechanism that scan across the image with sliding windows to find the candidate signs. This kind of detection mechanism is not covered in this project.