Poribohon-BD: Bangladeshi local vehicle image dataset with annotation for classification

• Main Authors: Shaira Tabassum, Sabbir Ullah, Nakib Hossain Al-nur, Swakkhar Shatabda
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Data in Brief
• Subjects: Vehicle image dataset; Image annotation; Data augmentation; Vehicle classification; Convolutional neural network; Computer vision
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920313470

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.