Event Title

Road Signs with AI powered applications

Session

Computer Science and Communication Engineering

Description

In this paper about Big Data we have experimented with the known neural network for image classification CNN (known as Convolutional Neural Network), where we build a model based on The German Traffic Sign Benchmark dataset and add certain configurations which include layers like convolutional, relu, pooling and fully connected layer. We continue training our model for a certain number of epochs, check the results and compare the performance by observing the values of accuracy and loss, during which time our model is improving itself through forward propagation and backpropagation, until we have a well-defined neural network that is good enough to detect features. The model accuracy we achieve is 96%. Since we achieved a good result on accuracy we continue on deploying our model on a desktop application with a simple graphical user interface which makes testing and using our model real easy and user friendly.

Keywords:

CNN, Traffic Sign Recognition, Neural Networks, Artificial Intelligence, Data Mining, Image Classification.

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-47-5

Location

UBT Kampus, Lipjan

Start Date

30-10-2021 12:00 AM

End Date

30-10-2021 12:00 AM

DOI

10.33107/ubt-ic.2021.389

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Oct 30th, 12:00 AM Oct 30th, 12:00 AM

Road Signs with AI powered applications

UBT Kampus, Lipjan

In this paper about Big Data we have experimented with the known neural network for image classification CNN (known as Convolutional Neural Network), where we build a model based on The German Traffic Sign Benchmark dataset and add certain configurations which include layers like convolutional, relu, pooling and fully connected layer. We continue training our model for a certain number of epochs, check the results and compare the performance by observing the values of accuracy and loss, during which time our model is improving itself through forward propagation and backpropagation, until we have a well-defined neural network that is good enough to detect features. The model accuracy we achieve is 96%. Since we achieved a good result on accuracy we continue on deploying our model on a desktop application with a simple graphical user interface which makes testing and using our model real easy and user friendly.