Visual analytics using deep learning : drowsiness detection using deep learning
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 bui...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140235 |
| _version_ | 1811694586165198848 |
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| author | Shao, Yewen |
| author2 | Yap Kim Hui |
| author_facet | Yap Kim Hui Shao, Yewen |
| author_sort | Shao, Yewen |
| collection | NTU |
| description | 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%. |
| first_indexed | 2024-10-01T07:09:55Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140235 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:09:55Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1402352023-07-07T18:49:37Z Visual analytics using deep learning : drowsiness detection using deep learning Shao, Yewen Yap Kim Hui School of Electrical and Electronic Engineering ekhyap@ntu.edu.sg Engineering::Electrical and electronic engineering 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%. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-27T08:05:26Z 2020-05-27T08:05:26Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140235 en P3035-182 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Shao, Yewen Visual analytics using deep learning : drowsiness detection using deep learning |
| title | Visual analytics using deep learning : drowsiness detection using deep learning |
| title_full | Visual analytics using deep learning : drowsiness detection using deep learning |
| title_fullStr | Visual analytics using deep learning : drowsiness detection using deep learning |
| title_full_unstemmed | Visual analytics using deep learning : drowsiness detection using deep learning |
| title_short | Visual analytics using deep learning : drowsiness detection using deep learning |
| title_sort | visual analytics using deep learning drowsiness detection using deep learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/140235 |
| work_keys_str_mv | AT shaoyewen visualanalyticsusingdeeplearningdrowsinessdetectionusingdeeplearning |