JUIVCDv1: development of a still-image based dataset for indian vehicle classification.

An automatic vehicle classification (AVC) system designed from either still images or videos has the potential to bring significant benefits to the development of a traffic control system. On AVC, numerous articles have been published in the literature. Over the years, researchers in this domain have created and used a variety of datasets, but most often, these datasets may not reflect the exact scenarios of the Indian subcontinent due to specific peculiarities of the road conditions, road congestion nature, and vehicle types usually seen in Indian subcontinent. The primary goal of this paper is to create a new still image dataset, called JUIVCDv1, which contains 12 different local vehicle classes that are collected using mobile cameras in a different way for developing an automated vehicle management system. We have also discussed the characteristics of the current datasets, and various other factors taken into account while creating the dataset for the Indian scenario. Apart from this, we have benchmarked the results on the developed dataset using eight state-of-the-art pre-trained convolutional neural network (CNN) models, namely Xception, InceptionV3, DenseNet121, MobileNetV2, and VGG16, NasNetMobile, ResNet50 and ResNet152. Among these, the Xception, InceptionV3 and DenseNet121 models produce the best classification accuracy scores of 0.94, 0.93 and 0.92 respectively. These models are further utilized to make an ensemble model to enhance the performance of the overall categorization model. Majority voting-based ensemble, Weighted average-based ensemble, and Sum rule-based ensemble approaches are used as ensemble models that give accuracy scores of 0.95, 0.94, and 0.94, respectively. [ABSTRACT FROM AUTHOR]