Abstract
The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.
Highlights
Today’s ore advanced technologies are furthering our goals and helping with automation in every field making the need for a human in those areas invalid, because a human is prone to making mistakes, but a machine in his/her place would certainly be more efficient, both in terms of speed and accuracy [34]
We have built a deep neural network model using Convolutional Neural Networks that have the capability to classify traffic signals that are present in the image based on its class
With the model that awe have developed, we were able to detect as well as classify traffic signals, which is very crucial to self-driving cars because it can otherwise lead to fatal accidents
Summary
Today’s ore advanced technologies are furthering our goals and helping with automation in every field making the need for a human in those areas invalid, because a human is prone to making mistakes, but a machine in his/her place would certainly be more efficient, both in terms of speed and accuracy [34]. Technologies such as Deep Learning and Machine Learning have evolved greatly in this time [2]. With the model that awe have developed, we were able to detect as well as classify traffic signals, which is very crucial to self-driving cars because it can otherwise lead to fatal accidents
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