Image recognition in self-driving cars using CNN

Abstract

The concept of neural networks has existed for over decades but was never considerably acknowledged as much as of today. The main reason happens to be “data.” To analyze a problem statement using neural networks, large data is required in its various forms and therefore it has not been instigated back in the day. But now, with today’s vast technology, neural networks have begun to take over some of the numerous machine learning applications with the help of huge datasets. In this research paper, a certain deep learning approach namely convolutional neural network (CNN) has been discussed which plays a major role in classifying and recognizing objects i.e., obstacles on the road. Earlier, computer-based algorithms have been followed for image processing in vehicles which seemed to be applicable to a certain extent. So much so, now with deep learning approaches, simpler yet faster networks can be implemented for a safe drive. Automatic vehicles such as Tesla which is examined to be “fully self-driving” nevertheless needs a driver to watch over the road at some particular point. This proves that there is not yet a fully controlled self-driving car created which can drive itself without a spectator. This appeal can be solved by means of image detection mechanisms using neural networks along with a programming language to deploy machine learning models at ease. The main objective is to develop a simple and accurate algorithm to make image recognition more precise for a better self-driving car.