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학술대회 Object Detection in Artworks Using Data Augmentation
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저자
전형주, 정순철, 최윤석, 김재우, 김진서
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1312-1314
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289321
협약과제
20IH3600, 근현대 미술품들의 디지털 데이터 확보 및 과학 기반 미술품 신뢰도 분석 지원 시스템 개발, 김진서
초록
Object detection is useful for authenticating artworks, and object detection methods based on deep learning have been actively studied in recent years. However, existing object detection methods cannot perform well on artworks, due to the lack of labeled artwork datasets. To overcome the problem, we propose a novel approach for object detection on artworks. The proposed approach consists of two steps. First, we apply neural style transfer to the existing images for data augmentation. Second, we train an object detection method on the augmented dataset. We evaluate the proposed approach on Brueghel and People-Art datasets. The experimental results show that our proposed approach successfully detects objects in artworks. Moreover, our approach achieved 40.6 mAP, which is 7.8 mAP higher than the conventional approach without data augmentation. It is expected that the proposed approach is of great benefit to art historians for analyzing a large number of artworks.
키워드
artwork, data augmentation, object detection, style transfer
KSP 제안 키워드
Data Augmentation, Detection Method, Novel approach, Object detection, Style transfer, deep learning(DL)