Feature pyramid networks for object detection
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Feature pyramid networks for object detection. / Lin, Tsung Yi; Dollár, Piotr; Girshick, Ross; He, Kaiming; Hariharan, Bharath; Belongie, Serge.
In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 06.11.2017, p. 936-944.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Feature pyramid networks for object detection
AU - Lin, Tsung Yi
AU - Dollár, Piotr
AU - Girshick, Ross
AU - He, Kaiming
AU - Hariharan, Bharath
AU - Belongie, Serge
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.
AB - Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.
UR - http://www.scopus.com/inward/record.url?scp=85041898381&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.106
DO - 10.1109/CVPR.2017.106
M3 - Conference article
AN - SCOPUS:85041898381
SP - 936
EP - 944
JO - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
JF - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -
ID: 301827160