Data augmentation using synthesized images for object detection

Hyunjun Jo, Yong Ho Na, Jae Bok Song

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    22 Citations (Scopus)

    Abstract

    Recently deep learning-based research has been conducted in various fields. Deep learning algorithms require vast amounts of data for good performance. Therefore, collecting such a huge amount of high-quality data is crucial to the deep learning-based methods. Data collection is simple but very time-consuming. To cope with this difficulty, in this study we propose a method to generate a dataset by synthesizing the images of background and object. Various images can be generated through post-processes such as adding noise and changing brightness to the images of objects obtained from different viewpoints. Furthermore, we do not need to manually annotate the dataset for object detection because we can calculate the parameters of the bounding boxes from the location and size of object images during the synthesis process. Faster R-CNN, one of the deep learning algorithms for object recognition, was used to verify the proposed method. The performance based on the dataset generated by the proposed method is comparable to that based on the real dataset.

    Original languageEnglish
    Title of host publicationICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
    PublisherIEEE Computer Society
    Pages1035-1038
    Number of pages4
    ISBN (Electronic)9788993215137
    DOIs
    Publication statusPublished - 2017 Dec 13
    Event17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of
    Duration: 2017 Oct 182017 Oct 21

    Publication series

    NameInternational Conference on Control, Automation and Systems
    Volume2017-October
    ISSN (Print)1598-7833

    Other

    Other17th International Conference on Control, Automation and Systems, ICCAS 2017
    Country/TerritoryKorea, Republic of
    CityJeju
    Period17/10/1817/10/21

    Bibliographical note

    Funding Information:
    This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 10067441)

    Publisher Copyright:
    © 2017 Institute of Control, Robotics and Systems - ICROS.

    Keywords

    • Data augmentation
    • Deep learning
    • Object detection
    • Synthesized images

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

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