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.

翻译：本文提出并行残差双向融合特征金字塔网络（PRB-FPN），用于快速且精确的单阶段目标检测。特征金字塔（FP）在近年来的视觉检测中广泛应用，然而FP的自顶向下路径由于池化偏移导致定位精度不足。随着更深层骨干网络的引入，FP的优势进一步被削弱。此外，它难以同时兼顾小目标与大目标的精确检测。针对上述问题，我们提出一种新的并行FP结构，采用双向（自顶向下与自底向上）融合及关联改进，以保留高质量特征实现精确定位。我们提供以下设计改进：（1）一种并行双向融合FP结构，包含自底向上融合模块（BFM），可同时高精度检测小目标和大目标。（2）一种拼接与重组（CORE）模块提供自底向上的特征融合路径，从而形成双向融合FP，能够恢复底层特征图中丢失的信息。（3）对CORE特征进一步纯化以保留更丰富的上下文信息。这种在自顶向下与自底向上路径中的CORE纯化仅需少量迭代即可完成。（4）在CORE中加入残差设计形成新的Re-CORE模块，便于训练并与多种更深或更浅骨干网络集成。所提出的网络在UAVDT17和MS COCO数据集上均达到最先进性能。代码已开源至https://github.com/pingyang1117/PRBNet_PyTorch。