  This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network
(PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid
(FP) is widely used in recent visual detection, however the top-down pathway of
FP cannot preserve accurate localization due to pooling shifting. The advantage
of FP is weakened as deeper backbones with more layers are used. In addition,
it cannot keep up accurate detection of both small and large objects at the
same time. To address these issues, we propose a new parallel FP structure with
bi-directional (top-down and bottom-up) fusion and associated improvements to
retain high-quality features for accurate localization. We provide the
following design improvements: (1) A parallel bifusion FP structure with a
bottom-up fusion module (BFM) to detect both small and large objects at once
with high accuracy. (2) A concatenation and re-organization (CORE) module
provides a bottom-up pathway for feature fusion, which leads to the
bi-directional fusion FP that can recover lost information from lower-layer
feature maps. (3) The CORE feature is further purified to retain richer
contextual information. Such CORE purification in both top-down and bottom-up
pathways can be finished in only a few iterations. (4) The adding of a residual
design to CORE leads to a new Re-CORE module that enables easy training and
integration with a wide range of deeper or lighter backbones. The proposed
network achieves state-of-the-art performance on the UAVDT17 and MS COCO
datasets. Code is available at https://github.com/pingyang1117/PRBNet_PyTorch.
