dc.contributor.author |
Kiwan, Mohamed Gamal el din mohamed |
|
dc.date.accessioned |
2021-01-27T06:46:39Z |
|
dc.date.available |
2021-01-27T06:46:39Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Copyright © 2021 MSA University. All Rights Reserved. |
en_US |
dc.identifier.uri |
http://repository.msa.edu.eg/xmlui/handle/123456789/4384 |
|
dc.description |
Computer sciences distinguished graduation projects 2020 |
en_US |
dc.description.abstract |
This project builds a truck detecting model for automatically supporting the traffic department Decide if the truck is overloaded or Normal-loaded to help the traffic department in controlling of high-way roads. We build the model based on convolutional neural network model. The dataset of the truck is constructed and hyper parameters modified of the convolutional neural network. A basic network model has successfully been trained by KERAS library. The model by KERAS library achieves 91.67% on overload / normal-overload truck classification, which isn't a bad result. we dived deeply in the model by changing the model to work by TENSORFLOW library. we optimized the model by TENSORFLOW library, the optimized TENSORFLOW model achieved 96% on the test set, that's better than that 91.67% of the KERAS model. |
en_US |
dc.description.sponsorship |
Dr. Ahmed Farouk |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
October University for Modern Sciences and Arts |
en_US |
dc.relation.ispartofseries |
COMPUTER SCIENCES DISTINGUISHED PROJECTS 2020; |
|
dc.subject |
October University for Modern Sciences and Arts |
en_US |
dc.subject |
University of Modern Sciences and Arts |
en_US |
dc.subject |
جامعة أكتوبر للعلوم الحديثة و الآداب |
en_US |
dc.subject |
MSA University |
en_US |
dc.subject |
Deep-learning detection |
en_US |
dc.title |
Deep-learning based trucks violation detection |
en_US |
dc.type |
Other |
en_US |
dc.Affiliation |
October University for modern sciences and Arts (MSA) |
|