Published: 1 October 2020| Version 2 | DOI: 10.17632/pwyyg8zmk5.2


Poribohon-BD is a vehicle dataset of 15 native vehicles of Bangladesh. The vehicles are: i) Bicycle, ii) Boat, iii) Bus, iv) Car, v) CNG, vi) Easy-bike, vii) Horse-cart, viii) Launch, ix) Leguna, x) Motorbike, xi) Rickshaw, xii) Tractor, xiii) Truck, xiv) Van, xv) Wheelbarrow. The dataset contains a total of 9058 images with a high diversity of poses, angles, lighting conditions, weather conditions, backgrounds. All of the images are in JPG format. The dataset also contains 9058 image annotation files. These files state the exact positions of the objects with labels in the corresponding image. The annotation has been performed manually and the annotated values are stored in XML files. LabelImg tool by Tzuta Lin has been used to label the images. Moreover, data augmentation techniques have been applied to keep the number of images comparable to each type of vehicle. Human faces have also been blurred to maintain privacy and confidentiality. The data files are divided into 15 individual folders. Each folder contains images and annotation files of one vehicle type. The 16th folder titled ‘Multi-class Vehicles’ contains images and annotation files of different types of vehicles. Poribohon-BD is compatible with various CNN architectures such as YOLO, VGG-16, R-CNN, DPM.



United International University


Computer Vision, Vehicle, Road Traffic Safety, Image Classification, Bangladesh, Public Transport, Convolutional Neural Network