Web Scraping Crawling-based Automatic Data Augmentation for Deep Neural Networks-based Vehicle Classifications

In this paper, we propose a novel data augmentation using the web scraping crawling for Deep Neural Networks (DNNs). First, we collect training data through the proposed web scraping crawler and automatically increase given data by the image processing techniques optimized for the data augmentation. In addition, for the evaluation, we compare the effect of each image processing technique through the cross-validation. In the simulation results, the validation accuracy of the DNN classifier using the augmented data through the optimal augmentation method was about 23.58% higher than that of the DNN classifier using original data.