Citation

Tabassum S, Ullah S, Al-Nur NH, Shatabda S. Data Brief 2020; 33: e106465.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.dib.2020.106465

PMID

33195776 PMCID

Abstract

Vehicle Classification has become tremendously important due to various applications such as traffic video surveillance, accident avoidance, traffic congestion prevention, bringing intelligent transportation systems. This article presents 'Poribohon-BD' dataset for vehicle classification purposes in Bangladesh. The vehicle images are collected from two sources: i) smartphone camera, ii) social media. The dataset contains 9058 labeled and annotated images of 15 native Bangladeshi vehicles such as bus, motorbike, three-wheeler rickshaw, truck, wheelbarrow. Data augmentation techniques have been applied to keep the number of images comparable to each type of vehicle. For labeling the images, LabelImg tool by Tzuta Lin has been used. Human faces have also been blurred to maintain privacy and confidentiality. The dataset is compatible with various CNN architectures such as YOLO, VGG-16, R-CNN, DPM. It is available for research purposes at https://data.mendeley.com/datasets/pwyyg8zmk5/2.

Language: en

Keywords

Computer vision; Convolutional neural network; Data augmentation; Image annotation; Vehicle classification; Vehicle image dataset