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Selective labeling using image super resolution for improving the efficiency of object detection in low-resolution oriental paintings
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저해상 동양화 객체 탐지의 효율성 향상을 위한 이미지 초고해상화 기반 선택적 레이블링 방법론

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Type
Academic journal
Author
Hyeyoung Moon (Kookmin University) Namgyu Kim (Kookmin University)
Journal
The Korean Society Of Computer And Information Journal of the Korea Society of Computer and Information Vol.27 No.9(Wn.222) KCI Accredited Journals
Published
2022.9
Pages
21 - 32 (12page)

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Selective labeling using image super resolution for improving the efficiency of object detection in low-resolution oriental paintings
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Image labeling must be preceded in order to perform object detection, and this task is considered a significant burden in building a deep learning model. Tens of thousands of images need to be trained for building a deep learning model, and human labelers have many limitations in labeling these images manually. In order to overcome these difficulties, this study proposes a method to perform object detection without significant performance degradation, even though labeling some images rather than the entire image. Specifically, in this study, low-resolution oriental painting images are converted into high-quality images using a super-resolution algorithm, and the effect of SSIM and PSNR derived in this process on the mAP of object detection is analyzed. We expect that the results of this study can contribute significantly to constructing deep learning models such as image classification, object detection, and image segmentation that require efficient image labeling.
#object detection #deep learning #image labeling #super resolution #SSIM #객체 탐지 #딥러닝 #이미지 레이블링 #초고해상화

Contents

Abstract
요약
Ⅰ. Introduction
Ⅱ. Related Research
Ⅲ. Proposed Method
Ⅳ. Experiment
Ⅴ. Conclusion
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