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Vehicle type validation for highway entrances using convolutional neural networks

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dc.contributor.author Juwanwadu, L.N.W.
dc.contributor.author Jayasiri, A.
dc.date.accessioned 2018-08-15T09:33:55Z
dc.date.available 2018-08-15T09:33:55Z
dc.date.issued 2018
dc.identifier.citation Juwanwadu,L.N.W. and Jayasiri,A. (2018). Vehicle type validation for highway entrances using convolutional neural networks. International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka. p.154. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/19023
dc.description.abstract Vehicle type validation for Highway entrances using convolutional neural networks is an approach taken to automate the highway toll systems of Sri Lanka. Available automated highway toll systems in the world use sensor-based validation systems to validate the vehicles that are entering the highways. Mainteneance cost of these systems is high. A vision-based validation system has not been implemented, as yet. This paper introduces a vision-based method to validate vehicles for highway systems which can reduce the cost while increasing the efficiency and safety. A Convolutional Neural Network (CNN) model was developed to achieve this objective. The CNN model employed here uses a binary classification to categorize vehicles as allowed vehicles and non-allowed vehicles for entering the highway. The model developed here showed 86.69% accuracy. The model was manually tested for different vehicle types using a GUI based application and all the test images were successfully classified into their classes. en_US
dc.language.iso en en_US
dc.publisher International Research Conference on Smart Computing and Systems Engineering - SCSE 2018 en_US
dc.subject Convolutional neural networks en_US
dc.subject Image classification en_US
dc.subject Machine Learning en_US
dc.subject Vehicle classification en_US
dc.subject Vehicle validation en_US
dc.title Vehicle type validation for highway entrances using convolutional neural networks en_US
dc.type Article en_US


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