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Distracted driver detection with deep convolution neural networks

This project aims to develop a python algorithm to detect the distracted activities while driving. National Highway traffic Safety Administration of United State (NHTSA) has been reported in 2015 around 3477 deaths cases and injuries to 391000 people because of distracted driving, with that distract...

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Main Author: Basubeit, Omar Gumaan Saleh
Format: text::Final Year Project
Language:English US
Published: 2023
Subjects:
Neural networks (Computer science)
Detectors
Distracted driving
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id my.uniten.dspace-27654
record_format dspace
spelling my.uniten.dspace-276542023-07-23T02:06:49Z Distracted driver detection with deep convolution neural networks Basubeit, Omar Gumaan Saleh Neural networks (Computer science) Detectors Distracted driving This project aims to develop a python algorithm to detect the distracted activities while driving. National Highway traffic Safety Administration of United State (NHTSA) has been reported in 2015 around 3477 deaths cases and injuries to 391000 people because of distracted driving, with that distracted driving considered as one of the main causes of car accidents. This project focuses to reduce manual and visual distractions by using visual dataset of 20,000 images divided to three sets: train set, validation set and test set. Unsafe activities are texting and talking on mobile phone, operating the radio, reaching behind to grab something, talking to passenger, drinking or eating and hair or makeup. To ensure having a high accuracy, the author decided to use deep learning technology and convolution neural networks (CNNs) in specific. The author created his algorithm and 12 Keras pre-trained models such as VGG16 and Xception, with adding 5 layers on the top of them. Moreover, this project compares Keras pre-trained models for two strategies: train only the classifier and train the top layers including the classifier. 2023-07-21T01:16:41Z 2023-07-21T01:16:41Z 2019 Resource Types::text::Final Year Project https://irepository.uniten.edu.my/handle/123456789/27654 en_US application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English US
topic Neural networks (Computer science)
Detectors
Distracted driving
spellingShingle Neural networks (Computer science)
Detectors
Distracted driving
Basubeit, Omar Gumaan Saleh
Distracted driver detection with deep convolution neural networks
description This project aims to develop a python algorithm to detect the distracted activities while driving. National Highway traffic Safety Administration of United State (NHTSA) has been reported in 2015 around 3477 deaths cases and injuries to 391000 people because of distracted driving, with that distracted driving considered as one of the main causes of car accidents. This project focuses to reduce manual and visual distractions by using visual dataset of 20,000 images divided to three sets: train set, validation set and test set. Unsafe activities are texting and talking on mobile phone, operating the radio, reaching behind to grab something, talking to passenger, drinking or eating and hair or makeup. To ensure having a high accuracy, the author decided to use deep learning technology and convolution neural networks (CNNs) in specific. The author created his algorithm and 12 Keras pre-trained models such as VGG16 and Xception, with adding 5 layers on the top of them. Moreover, this project compares Keras pre-trained models for two strategies: train only the classifier and train the top layers including the classifier.
format Resource Types::text::Final Year Project
author Basubeit, Omar Gumaan Saleh
author_facet Basubeit, Omar Gumaan Saleh
author_sort Basubeit, Omar Gumaan Saleh
title Distracted driver detection with deep convolution neural networks
title_short Distracted driver detection with deep convolution neural networks
title_full Distracted driver detection with deep convolution neural networks
title_fullStr Distracted driver detection with deep convolution neural networks
title_full_unstemmed Distracted driver detection with deep convolution neural networks
title_sort distracted driver detection with deep convolution neural networks
publishDate 2023
_version_ 1806428444946333696
score 13.211869

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