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공공누리This item is licensed Korea Open Government License

Title
CycleGAN을 이용한 야간 상황 물체 검출 알고리즘
Author(s)
이용황원준조상흠이상환나재민김영빈박민우
Publisher
한국멀티미디어학회
Publication Year
2019-01-15
Abstract
Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.
Keyword
CycleGAN; Data Sampling; Image-to-Image Translation
Journal Title
멀티미디어학회 논문지;
Citation Volume
22
ISSN
1229-7771
DOI
10.9717/kmms.2019.22.1.044
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/16784
Fulltext
 https://scienceon.kisti.re.kr/srch/selectPORSrchArticle.do?cn=JAKO201911338887012
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