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Real-Time Vehicle Classification Using CNN

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dc.contributor.author Jahan, Nusrat
dc.contributor.author Islam, Saiful
dc.contributor.author Foysal, Md. Ferdouse Ahmed
dc.date.accessioned 2021-10-02T10:12:53Z
dc.date.available 2021-10-02T10:12:53Z
dc.date.issued 2020
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6234
dc.description.abstract Convolutional Neural Network (CNN) is a model of artificial neural networks that has grown to be most well known in computer vision assignment. In this paper work, we presented convolutional neural network for classifying four types of common vehicle in our country. Vehicle classification plays a vital role of various application such as surveillance security system, traffic control system. We addressed these issues and fixed an aim to find a solution to reduce road accident due to traffic related cases. The greatest challenge of computer vision is to achieve effective results to implement a system due to variation in shapes and colors of data. To classify the vehicle we used two methods feature extraction and classification. These two methods can straightforwardly performed by convolutional neural network. The method shows quite good performance on real-time standard dataset. Our mentioned method able to reach 97% accuracy in case of vehicle classification. en_US
dc.language.iso en_US en_US
dc.publisher Scopus en_US
dc.subject Vehicle en_US
dc.subject Machine learning en_US
dc.subject Image processing en_US
dc.subject CNN en_US
dc.subject Computer Vision en_US
dc.title Real-Time Vehicle Classification Using CNN en_US
dc.type Article en_US


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