Nowadays, there is a lot of attention being given to the ability of the car to drive itself. One of the many important aspects for a self driving car is the ability for it to detect traffic signs in order to provide safety and security for the people not only inside the car but also outside of it. The traffic environment consists of different aspects whose main purpose is to regulate flow of traffic, make sure each driver is adhering to the rules so as to provide a safe and secure environment to all the parties concerned.
The main objective of our project is to design and construct a computer based system which can automatically detect the road signs so as to provide assistance to the user or the machine so that they can take appropriate actions
We have used convolutional neural networks (CNN) to classify the traffic signs and we used color based segmentation to extract/crop signs from images. We deployed the model on web using flask, Html/css and java script .
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The problem of traffic sign recognition is twofold:
Extracting a potential traffic sign from an image. Traffic signs are designed such that they appear unique and easily identifiable to the human eye. Traffic signs in the United States of America are of 3 main colors: Red, White, and Yellow. Other colors like orange and blue are also used. In our approach we concentrate on Red, White, and Yellow traffic signs.
We cropped the image to the area of interest where the focus is only on the signs. Once we have refined the set of areas of interest, we use the convolutional neural network which we are going to build in the next step to predict the type of this sign (or if it is not a sign).
The data set was collected from kaggle , which have about 43 diffrent classes . The test set was obtained by splitting the whole dataset into 80% train data and 20 % validation and test data. Once the CNN has been trained, it is used to predict the sign of the contours obtained.
Accuracy with the test data : 0.9403
