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Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Hasan, Mahibul Wang, Zhijie Hussain, Muhammad Ather Iqbal Fatima, Kaniz |
| Copyright Year | 2021 |
| Description | Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and |
| Starting Page | 7545 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21227545 |
| Journal | Sensors |
| Issue Number | 22 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-13 |
| Access Restriction | Open |
| Subject Keyword | Sensors Transportation Science and Technology Native Vehicle Type Classification Deshi-bd Vehicle Dataset Deep Learning Transfer Learning Resnet-50 |
| Content Type | Text |
| Resource Type | Article |
