Publication: Vehicle classification from images using deep learning
| dc.contributor.author | Mohd 'Adli Faisal Hazim Abdullah | |
| dc.date.accessioned | 2024-04-23T01:20:38Z | |
| dc.date.available | 2024-04-23T01:20:38Z | |
| dc.date.issued | 2018 | |
| dc.description | HE151.M63 2018 | |
| dc.description.abstract | Real-time traffic information is an important part of transportation system to analyse and solve traffic problem. The goal is to avoid serious traffic congestion at highway tollbooth as the total number of road vehicles is expected to increase. The solution to improve the safety of travelling and to make traffic control and management better, real- time traffic information, number of passing vehicles, travel time, and vehicle classification data are necessary for road users and traffic administrations. This research was applying Deep Learning method to achieve classification objectives. This research also used Convolutional Deep Neural Network (CDNN) for training the machine learning. From this research, more than 90.0% of validation accuracy are achieve. The machine also achieve high rate of correct prediction on vehicle classes. | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/31914 | |
| dc.language.iso | en | |
| dc.subject | Transportation and communication | |
| dc.title | Vehicle classification from images using deep learning | |
| dc.type | Resource Types::text::Final Year Project | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 113 | |
| oaire.citation.startPage | 1 | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# |