Dataset of vehicle images for Indonesia toll road tariff classification
| Autor: | Wisnu Jatmiko, Grafika Jati, Ananto Tri Sasongko, Mohamad Ivan Fanany |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Truck
Computer science Tariff lcsh:Computer applications to medicine. Medical informatics Residual neural network Transport engineering 03 medical and health sciences 0302 clinical medicine lcsh:Science (General) 030304 developmental biology Vehicle image 0303 health sciences Multidisciplinary business.industry Deep learning Toll road Classification Class (biology) Transfer learning Axle Fine-tuning Computer Science lcsh:R858-859.7 Artificial intelligence business 030217 neurology & neurosurgery lcsh:Q1-390 Dataset |
| Zdroj: | Data in Brief Data in Brief, Vol 32, Iss, Pp 106061-(2020) |
| ISSN: | 2352-3409 |
| Popis: | Vehicle classifications with different methods have been applied for many purposes. The data provided in this article is useful for classifying vehicle purposes following the Indonesia toll road tariffs. Indonesia toll road tariff regulations divide vehicles into five groups as follows, group-1, group-2, group-3, group-4, and group-5, respectively. Group-1 is a class of non-truck vehicles, while group-2 to group-5 are classes of truck vehicles. The non-truck class consists of the sedan, pick-up, minibus, bus, MPV, and SUV. Truck classes are grouped based on the number of truck's axles. Group-2 is a class of trucks with two axles, a group-3 truck with three axles, a group-4 truck with four axles, and a group-5 truck with five axles or more. The dataset is categorized into five classes accordingly, which are group-1, group-2, group-3, group-4, and group-5 images. The data made available in this article observes images of vehicles obtained using a smartphone camera. The vehicle images dataset incorporated with deep learning, transfer learning, fine-tuning, and the Residual Neural Network (ResNet) model can yield exceptional results in the classification of vehicles by the number of axles. |
| Databáze: | OpenAIRE |
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