지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.1
- 발행연도
- 2022.3
- 수록면
- 14 - 24 (11page)
- DOI
- 10.5626/JCSE.2022.16.1.14
이용수
초록· 키워드
오류제보하기
The feature pyramid network (FPN) enhances the localization accuracy and detection performance of small objects using multiple scales of the features. FPN adopts lateral connections and a top-down pathway to make low-level features semantically more meaningful. However, it uses only single-scale features to pool regions of interest (RoIs) when detecting objects. In this study, we showed that single-scale RoI pooling may not be the best solution for accurate localization and proposed multi-scale RoI pooling to improve the minor drawbacks of the FPN. The proposed method pools RoIs from three feature levels and concatenates the pooled features to detect objects. Thus, the FPN with multi-scale RoI pooling, called FPN+, detects objects by taking into account all information scattered across three feature levels. FPN+ improved the FPN by 2.81 and 1.1 points in COCO-style average precision (AP) when tested on PASCAL VOC 2007 test and COCO 2017 validation datasets, respectively.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. DRAWBACKS OF FPN
IV. PROPOSED METHOD
V. EXPERIMENTAL RESULTS
VI. CONCLUSION
REFERENCES
참고문헌 (27)
참고문헌 신청
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 580-587.
R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015, pp. 1440-1448.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 779-788.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015, pp. 1440-1448.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 779-788.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
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