Traffic Sign Recognition

Writeup/README

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Build a Traffic Sign Recognition Project

The goals / steps of this project are the following:

• Load the data set
• Explore, summarize and visualize the data set
• Design, train and test a model architecture
• Use the model to make predictions on new images
• Analyze the softmax probabilities of the new images
• Summarize the results with a written report

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

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You are reading my Writeup/README. Here is a link to my project code Here is a link to my report.html

Data Set Summary & Exploration

1. Basic summary of the data set.

I used "shape" and "len()" to calculate summary statistics of the traffic signs data set:

• The size of training set is 34799
• The size of the validation set is 4410
• 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

2. Exploratory visualization of the dataset.

Here is an exploratory visualization of the data set. It is a bar chart showing the distribution of the data.

Design and Test a Model Architecture

1. Preprocessed the image data.

As a first step, I decided to convert the images to grayscale because this task is color independent, grayscale can decrease the computations and train time.

Here is an example of a traffic sign image before and after grayscaling.

2. Model architecture

My final model consisted of the following layers:

Layer	Description
Input	32x32x1 Gray image
Convolution 5x5	1x1 stride, VALID padding, outputs 32x32x64
RELU
Max pooling	2x2 stride, outputs 14x14x20
Convolution 3x3	1x1 stride, VALID padding, outputs 12x12x40
RELU
Dropout	50% keep chance
Convolution 3x3	1x1 stride, VALID padding, outputs 10x10x80
RELU
Max pooling	2x2 stride, outputs 5x5x80
Flatten	outputs 2000
Fully connected	outputs 120
RELU
Fully connected	outputs 84
RELU
Fully connected	outputs 43

3. Trained model

To train the model, I used

Hyperparameter	Description
epochs	40
batch size	128
dropout probabilities	50%
learning rate	0.001
optimizer	Adam

Adam is adaptive optimizer.

4. results and discuss

My final model results were:

• training set accuracy of 0.999
• validation set accuracy of 0.964
• test set accuracy of 0.948

I choose a well known architecture "LeNet". I add "dropout layer" to prevent overfit.

Test a Model on New Images

1. Test five new German traffic signs

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

The fourth image might be difficult to classify because it has a different shape and some watermarks.

2. Predictions results on these five images

Here are the results of the prediction:

Image	Prediction
Speed limit (30km/h)-1	Speed limit (30km/h)-1
No entry-17	No entry-17
Road work-25	Road work-25
Road work-25	Keep right-38
Right-of-way at the next intersection-11	Right-of-way at the next intersection-11

The model was able to correctly guess 4 of the 5 traffic signs, which gives an accuracy of 80%.

Why was the fourth image predicted to 25? We can see the distribution plot of the data above. The data with 25 label is more than others, it may lead biased toward the classes with more images. We can use data augmentation to increase the less images and balance the distribution to decrease the bias.

3. Top 5 softmax probabilities for each image

The code for making predictions on my final model is located in the 14th cell of the Ipython notebook.

For the correct predicted image, the model is very sure. For the fourth image, the model is not very sure.
The top five soft max probabilities were