object Classification of Traffic Signals Using Neural Network

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Srinivasan P, et. al.

Abstract

In today’s world AI(Artificial Intelligence) and computer vision application are inevitable. There are so many applications based on AI and computer vision which are developing now to make more improvement in human’s daily routine and lifestyle. It has application in several domains like medical, researcher, defence etc. For classifying traffic sign board photos, we used a computer vision programme. Our project's main goal is to create a model that can distinguish objects. Image classification model can be created with neural network or can be created using convolutional neural network. To define, we used various forms of traffic sign boards in this process. Our model is trained using 43 different types of traffic sign board videos. Once model trained, we can be able to use this model to classify other images too. In this model we used python programming for building our model and used tensor flow, keras frame works for various processes. It’s also can be used in automation cars. There are two popular method to made image classification (or) object detection in neural network and CNN(convolutional neural network).Each method have their own advantage and disadvantage. CNN is very useful in object classifier. CNN usually consists of number of layers depend upon the application and accuracy of a model required. So we used CNN model in our project to classify images.

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