Vehicle Make And TypeClassification Using Keras Deep Neural Network
In this era of globalization, sensors are no longer limited to physical sensors. The development of new technologies enables artificial intelligence (AI) to also function as a higher accuracy sensor. Many countries are currently facing sudden development in their non-rural areas. This situation has...
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my.uniten.dspace-205682023-05-04T18:12:46Z Vehicle Make And TypeClassification Using Keras Deep Neural Network Sorfina Jasmin Binti Mohtar Vehicle Keras Deep Neural Network In this era of globalization, sensors are no longer limited to physical sensors. The development of new technologies enables artificial intelligence (AI) to also function as a higher accuracy sensor. Many countries are currently facing sudden development in their non-rural areas. This situation has lead to the demand for the public safety system to be upgraded and the traffic conditions to be improvised. This project provides a solution in smart traffic monitoring, by classifying the make and type of Malaysian vehicles and non-Malaysian vehicles with an accuracy rate of at least 80%. The project implements Keras architecture with Tensorflow backend applied in Python programming, in the execution of Convolutional Neural Network (CNN). The output of this project highlights the different make and type of vehicles and can be implemented with both image and video input. 2023-05-03T15:06:02Z 2023-05-03T15:06:02Z 2019-10 https://irepository.uniten.edu.my/handle/123456789/20568 en application/pdf |
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Vehicle Keras Deep Neural Network Sorfina Jasmin Binti Mohtar Vehicle Make And TypeClassification Using Keras Deep Neural Network |
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In this era of globalization, sensors are no longer limited to physical sensors. The development of new technologies enables artificial intelligence (AI) to also function as a higher accuracy sensor. Many countries are currently facing sudden development in their non-rural areas. This situation has lead to the demand for the public safety system to be upgraded and the traffic conditions to be improvised. This project provides a solution in smart traffic monitoring, by classifying the make and type of Malaysian vehicles and non-Malaysian vehicles with an accuracy rate of at least 80%. The project implements Keras architecture with Tensorflow backend applied in Python programming, in the execution of Convolutional Neural Network (CNN). The output of this project highlights the different make and type of vehicles and can be implemented with both image and video input. |
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Sorfina Jasmin Binti Mohtar |
author_facet |
Sorfina Jasmin Binti Mohtar |
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Sorfina Jasmin Binti Mohtar |
title |
Vehicle Make And TypeClassification Using Keras Deep Neural Network |
title_short |
Vehicle Make And TypeClassification Using Keras Deep Neural Network |
title_full |
Vehicle Make And TypeClassification Using Keras Deep Neural Network |
title_fullStr |
Vehicle Make And TypeClassification Using Keras Deep Neural Network |
title_full_unstemmed |
Vehicle Make And TypeClassification Using Keras Deep Neural Network |
title_sort |
vehicle make and typeclassification using keras deep neural network |
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2023 |
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1806425853528113152 |
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13.222552 |