DATA AUGMENTATION FOR A LEARNING-BASED VEHICLE MAKE-MODEL AND LICENSE PLATE MATCHING SYSTEM

Abstract

The most important requirement for deep learning algorithms to run with a low error ratio is the realization of the training process with a sufficient amount of data. Using synthetic data is one of the most common approaches when the data set is not enough for training. Synthetic data production must be based on a real dataset to improve the prediction and classification abilities of the deep learning algorithms. The enrichment of the existing dataset using different techniques such as modified copies of existing data is called data augmentation. It can sometimes be difficult to generate enough datasets according to the type of problem, especially in image classification. In such cases, a dataset can be generated by duplicating and/or modifying existing pictures of the objects. In this study, data augmentation for a learning-based vehicle make-model and license plate matching system has been performed and a new vehicle image dataset has been generated. The proposed approach which has been used in creating the dataset is presented in detail. The generated new vehicle image dataset is available to developers as open-source.