We propose 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 weaken as deeper backbones with more layers are used. To address this
issue, 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. Our method is particularly suitable for detecting
small objects. 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 purification is performed with CORE
in a few iterations in both top-down and bottom-up pathways. (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 UAVDT17 and MS
COCO datasets.Comment: accepted by IEEE transactions on Image Processin