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A Novel Dataset Generating Method for Fine-Grained Vehicle Classification with CNN
Shaoyong Yu,Zhijun Song,Songzhi Su,Wei Li,Yun Wu,Wenhua Zeng 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.6
We focus on the issue of dataset generation for fine-grained vehicle classification with CNN. Traditionally, to build a large dataset, images must be first collected manually, and then be annotated with a lot of effort. All these work are time-consuming and cost-prohibitive. In this work we propose a novel method that can generate massive images automatically, and these generated images need no annotation. An AutoCAD 3D model of a car of specified make and model is imported into our system, and then images of different views of the car are generated, these images can describe all the details of a car. By taking these images as training dataset, we use a Convolutional Neural Network to train a model for fine-grained vehicle classification. Experimental results show that these images generated virtually by 3D model indeed work as effective as real images.