Deep learning for mobile phone detection while driving by image recognition

Majority of the existing works are based on in-car camera system mounted inside the vehicles, which is more suitable to be implemented as driver assistance system. This is because most of the driver assistance systems only are available for advanced cars. Besides that, a large amount of training dat...

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Main Author:	Yeoh, Mei Hwei
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93010/1/YeohMeiHweiMSKE2020.pdf
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id	my-utm-ep.93010
record_format	uketd_dc
spelling	my-utm-ep.930102021-11-07T06:00:24Z Deep learning for mobile phone detection while driving by image recognition 2020 Yeoh, Mei Hwei TK Electrical engineering. Electronics Nuclear engineering Majority of the existing works are based on in-car camera system mounted inside the vehicles, which is more suitable to be implemented as driver assistance system. This is because most of the driver assistance systems only are available for advanced cars. Besides that, a large amount of training datasets is required to train the driver monitoring system. However, there are no existing datasets that are captured based on camera system mounted outside the vehicles, thus the total amount of dataset acquired is limited for this project. Therefore, the aim of conducting this thesis is to develop a camera-based automated image recognition system that is mounted outside the vehicles for detecting the driver using mobile phones for calling while driving. Since there are no datasets available for this project, the images are captured and collected using Fujifilm XT 10 with XF 35mm f2 lens at overhead bridge nearby Spice Arena, Penang, Malaysia. The captured time is from 5pm to 7.30 pm. There are a total of 2,340 images are captured and collected. However, about 42 % of the captured images are discarded, left with1,348 images are applicable to be the dataset. The proposed system framework is developed based on Faster R-CNN with Inception-V2 architecture by fine tuning the training configuration parameters. The model is proposed to train up to 20,000 steps with the total loss is less than 0.07. The duration for the training process is about 64 hours. In terms of performance evaluation of the model, it is based on detection evaluation metrics applied by COCO. It shows that the mAP for Intersection Over Union threshold of 0.50 obtained 97.75% for the model in localizing the object along with the classes and the mAR with 10 detections per image obtained 72.03% for the model in classifying the object. In terms of overall detection accuracy, it obtained 88.71% for the accuracy. 2020 Thesis http://eprints.utm.my/id/eprint/93010/ http://eprints.utm.my/id/eprint/93010/1/YeohMeiHweiMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135888 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Yeoh, Mei Hwei Deep learning for mobile phone detection while driving by image recognition
description	Majority of the existing works are based on in-car camera system mounted inside the vehicles, which is more suitable to be implemented as driver assistance system. This is because most of the driver assistance systems only are available for advanced cars. Besides that, a large amount of training datasets is required to train the driver monitoring system. However, there are no existing datasets that are captured based on camera system mounted outside the vehicles, thus the total amount of dataset acquired is limited for this project. Therefore, the aim of conducting this thesis is to develop a camera-based automated image recognition system that is mounted outside the vehicles for detecting the driver using mobile phones for calling while driving. Since there are no datasets available for this project, the images are captured and collected using Fujifilm XT 10 with XF 35mm f2 lens at overhead bridge nearby Spice Arena, Penang, Malaysia. The captured time is from 5pm to 7.30 pm. There are a total of 2,340 images are captured and collected. However, about 42 % of the captured images are discarded, left with1,348 images are applicable to be the dataset. The proposed system framework is developed based on Faster R-CNN with Inception-V2 architecture by fine tuning the training configuration parameters. The model is proposed to train up to 20,000 steps with the total loss is less than 0.07. The duration for the training process is about 64 hours. In terms of performance evaluation of the model, it is based on detection evaluation metrics applied by COCO. It shows that the mAP for Intersection Over Union threshold of 0.50 obtained 97.75% for the model in localizing the object along with the classes and the mAR with 10 detections per image obtained 72.03% for the model in classifying the object. In terms of overall detection accuracy, it obtained 88.71% for the accuracy.
format	Thesis
qualification_level	Master's degree
author	Yeoh, Mei Hwei
author_facet	Yeoh, Mei Hwei
author_sort	Yeoh, Mei Hwei
title	Deep learning for mobile phone detection while driving by image recognition
title_short	Deep learning for mobile phone detection while driving by image recognition
title_full	Deep learning for mobile phone detection while driving by image recognition
title_fullStr	Deep learning for mobile phone detection while driving by image recognition
title_full_unstemmed	Deep learning for mobile phone detection while driving by image recognition
title_sort	deep learning for mobile phone detection while driving by image recognition
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93010/1/YeohMeiHweiMSKE2020.pdf
_version_	1747818626015035392

Deep learning for mobile phone detection while driving by image recognition

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