Training and Testing Object Detectors with Virtual Images

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specif...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Tian, Yonglin, Li, Xuan, Wang, Kunfeng, Wang, Fei-Yue
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.12.2017
Subjects
Annotations
Computer vision
Datasets
Deformation
Detectors
Flaw detection
Formability
Machine learning
Occlusion
Sensors
Training
Online AccessGet full text

Cover

More Information
Summary:In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
ISSN:2331-8422