Traffic Sign Recognition using Deep Learning (Python)

What is Traffic Sign Recognition?

There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.

In this project, we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

For this project, we are using the German Traffic Sign Recognition Benchmark (GTSRB) Dataset available at Kaggle.

The dataset contains more than 50,000 images of different traffic signs. It is further classified into 43 different classes. The dataset is quite varying, some of the classes have many images while some classes have few images. The size of the dataset is around 300 MB. The dataset has a train folder which contains images inside each class and a test folder which we will use for testing our model.

Firstly, the data is trained. For this recognition, we build a CNN model. For the project we will using Python, TensorFlow, Keras, Matplotlib, Scikit-learn, Pandas, PIL, Image Classification and Visual Studio Code.

Let’s earn some knowledge about programming language, libraries and editor used to build this project.

• Python is an interpreted, high-level and general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant whitespace.
• TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. Tensorflow is a symbolic math library based on dataflow and differentiable programming.
• Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Up until version 2.3 Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.
• Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. SciPy makes use of Matplotlib.
• Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines.
• Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.
• PIL (Python Imaging Library) is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats.
• Visual Studio Code is a freeware source-code editor made by Microsoft for Windows, Linux and macOS. Features include support for debugging, syntax highlighting, intelligent code completion, snippets, code refactoring, and embedded Git.
• Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labelled example photos. Early computer vision models relied on raw pixel data as the input to the model.

Our approach to building this traffic sign classification model is discussed in four steps:

• Explore the dataset
• Build a CNN model
• Train and validate the model
• Test the model with test dataset

By extracting dataset the files into a folder such that you will have a train, test and a meta folder.

Our ‘train’ folder contains 43 folders each representing a different class. The range of the folder is from 0 to 42. With the help of the OS module, we iterate over all the classes and append images and their respective labels in the data and labels list.

The architecture of our CNN model is:

• 2 Conv2D layer (filter=32, kernel_size=(5,5), activation=”relu”)
• MaxPool2D layer ( pool_size=(2,2))
• Dropout layer (rate=0.25)
• 2 Conv2D layer (filter=64, kernel_size=(3,3), activation=”relu”)
• MaxPool2D layer ( pool_size=(2,2))
• Dropout layer (rate=0.25)
• Flatten layer to squeeze the layers into 1 dimension
• Dense Fully connected layer (256 nodes, activation=”relu”)
• Dropout layer (rate=0.5)
• Dense layer (43 nodes, activation=”softmax”)

We compile the model with Adam optimizer which performs well and loss is “categorical_crossentropy” because we have multiple classes to categorise.

Flow Chart

Graph of Accuracy and Loss

Traffic Signs Classifier GUI

Result

The recognition system works properly. The values of all the attributes were properly pre-processed. After all the pre-processing was completed, model was implemented and it was trained using train data. Our accuracy was found to be around .

Conclusion

In this project we have successfully classified the traffic signs classifier with accuracy and also visualized how our accuracy and loss changes with time, which is pretty good from a simple CNN model.

GitHub Project Link

yashptl2611/Traffic-Sign-Recognition-using-CNN