Traffic recognition using python

Firstly, let me tell you a bit about the Traffic sign Recognition system. It is the procedure of naturally perceiving traffic signs along the street, including speed limit signs, caution signs, blend signs, and so forth. Being able to automatically recognize traffic signs empowers us to construct “smarter cars”. Traffic signs are a vital piece of our street and road infrastructure. They give basic data/info, at times convincing suggestions, for street clients, which expects them to change their driving conduct to ensure they stick to whatever street guideline at present authorized. Without such valuable signs, we would in all likelihood be confronted with more mishaps, as drivers would not be given basic input on how quickly they could securely go, or educated about street works, sharp turn, or school intersections ahead. Millions of people die on roads each year and this number would be a lot higher without our street signs. Normally, autonomous vehicles must also abide by road legislation and in this way perceive and comprehend traffic signs. And a project like this system can be pretty useful for the driver and to avoid any kind of mishappening and can have a safe and healthy drive. Generally, vision strategies were utilized to identify and order traffic signs. And I think we need this kind of implementation in this modern age.

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Project Description

In this project, we will build a deep neural network model that can order traffic signs present in the picture into various classifications. With the help of this model, we are able to read and understand/comprehend the traffic signs which are a very important task for all autonomous/independent vehicles.

1. This project requires some knowledge on topics like Python, Keras and CNN and some python library Matplotlib, Scikit-learn, Pandas, PIL and image classification.
2. Python-For programming purposes, we are going to utilize Python as the Programming language. Python is easy to learn and work on with the language. It is an elevated level, broadly useful programming and profoundly intruded on language.
3. CNN- The convolutional neural system is a class of profound neural systems, most generally applied to break down visual symbolism. CNN is a class of significant neural frameworks, most usually applied to separate visual imagery.ep neural frameworks, most routinely applied to explore visual imagery.
4. Build CNN- some fundamental steps of the task contains-

• Convolution
• Pooling
• Flattening

5. Keras-Keras is a significant level neural systems API, equipped for running over Tensorflow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML and CNTK. It empowers quick experimentation through an elevated level, easy to use, particular and extensible API. Keras can likewise be run on both CPU and GPU.
6. Point to be noted, “Programmers can explore different avenues regarding the programming language as indicated by their solace level and information" and can change the previously mentioned language as per them. 

Project Implementation-

1. First, install the necessary packages for the project.
2. Now, Download the dataset from the internet.
3. Explore the dataset & store all the images and their labels into lists.
4. To classify the images into their separate classes, we will build a CNN model and CNN is the best technique for image classification.
5. Now it's time to build and train the model.
6. Our model is prepared, now we need to test the model.
7. Now, we are going to build a graphical user interface for our traffic signs classifier with the help of Tkinter. (Tkinter is a GUI toolkit in the standard python library)
8. Lastly, our model is ready to run.

Software Requirement -

-Programming language -  Python

-Operating System - any os like a window, ubuntu.

By working on this project you can learn- 

     -  Python libraries

    -  CNN

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• Python
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Traffic recognition using python