Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140235
Title: Visual analytics using deep learning : drowsiness detection using deep learning
Authors: Shao, Yewen
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: P3035-182
Abstract: The rising demand for remote working and learning makes the efficiency of undisciplined individuals an issue. Deep learning is a truly disruptive technology in the field of machine learning, and it is robust in data processing, especially data classification. This objective of this project is to build a Convolutional Neural Network for drowsiness detection features using deep learning technique. Transfer learning approach with pre-trained VGG16 model was adopted in this project to achieve this objective. Controlled training was conducted to find the optimal set of parameters setting values. The testing of different learning rates, mini-batch sizes, optimizers, regularizers were performed, and application of data augmentation. The resultant CNN is capable to detect drowsiness state images with an average accuracy of 68.46%.
URI: https://hdl.handle.net/10356/140235
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Final Report_ShaoYewen.pdf
  Restricted Access
2.2 MBAdobe PDFView/Open

Page view(s)

227
Updated on Dec 3, 2022

Download(s) 50

52
Updated on Dec 3, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.