A Transfer Learning Approach with Modified VGG16 for Driving Behavior Detection in Intelligent Transportation Systems
| dc.contributor.author | Nur, Abdullah Hafez | |
| dc.date.accessioned | 2024-06-12T06:05:52Z | |
| dc.date.available | 2024-06-12T06:05:52Z | |
| dc.date.issued | 2024-06 | |
| dc.description | This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in B.Sc. Engineering, 2024 Cataloged from the PDF version of the internship report | |
| dc.description.abstract | The complex nature of human behaviour and the numerous factors contributing to distractions on the road, particularly in terms of the limited ability to provide precise and timely early warnings, are addressed in this paper through the introduction of a novel method. The proposed method aims to enhance the detection of distracted driver behaviour using modified CNN transfer learning. By employing transfer learning within the Convolutional Neural Networks framework, the method seeks to tackle the intricate challenges associated with identifying distracted driving behaviours. The experimental results demonstrate an overall accuracy of approximately 99%, with the highest achieved accuracy reaching 99.46% on a publicly available dataset. This highlights the effectiveness of the proposed approach in significantly improving the precision of distracted driver behaviour detection | |
| dc.identifier.uri | http://dspace.iiuc.ac.bd/handle/123456789/8254 | |
| dc.language.iso | en | |
| dc.publisher | International Islamic University Chittagong | |
| dc.title | A Transfer Learning Approach with Modified VGG16 for Driving Behavior Detection in Intelligent Transportation Systems | |
| dc.type | Thesis |