Parallel Feature Pyramid Network for Object Detection

Seung Wook Kim, Hyong Keun Kook, Jee Young Sun, Mun Cheon Kang, Sung Jea Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)


Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of CNN feature layers often limit the detection performance, especially on small objects. To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth. First, we adopt spatial pyramid pooling and some additional feature transformations to generate a pool of feature maps with different sizes. In PFPNet, the additional feature transformation is performed in parallel, which yields the feature maps with similar levels of semantic abstraction across the scales. We then resize the elements of the feature pool to a uniform size and aggregate their contextual information to generate each level of the final FP. The experimental results confirmed that PFPNet increases the performance of the latest version of the single-shot multi-box detector (SSD) by mAP of 6.4% AP and especially, 7.8% AP small on the MS-COCO dataset.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Martial Hebert, Yair Weiss
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783030012274
Publication statusPublished - 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sept 82018 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Conference on Computer Vision, ECCV 2018

Bibliographical note

Funding Information:
communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (2014-0-00077, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis).

Funding Information:
Acknowledgements. This work was supported by Institute for Information &

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.


  • Feature pyramid
  • Real-time object detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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