Traffic Sign Recognition Using Machine Learning

Author : Dr. Ratna babu P, B. Kavitha, N. Venkata Lakshmi, S. Madhu, G. Sivan Narayana

With advancement in artificial intelligence (AI), Deep learning models are used to imitate the actions of human beings. These activities of personage are controlled by their brain and similar to that, machines are capable of data processing, decision making, speech recognition and language translations just like human beings. One of the applications of deep learning includes Autonomous Vehicles design i.e., driver less cars. To implement this, we need an automatic traffic sign recognition (TSR) model. These models are designed with the use of convolutional neural networks (CNN). The main task of this model is to extract the various features of the different traffic sign images and classify according to unique categories. This paper includes a comprehensive review of various models that can be used for classifying traffic signs. Researchers have applied various CNN models to predict the class of traffic sign and these are proven to be better than machine learning algorithms. CNN works as a feed forward neural network which has been stimulated from animal visual cortex.

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