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:本文建议同时使用平行残存双向双向双向双向双向双向双向双向双向双向双向(上至下至上)聚合及相关改进,以保留高质量特性,实现准确的本地化。 功能自上至下路径在最近的视觉检测中被广泛使用, 但是由于集合转移, 无法保存准确的本地化。 功能自上至下路径的功能自上至下路径, 功能自上至下都无法保持准确的本地化。 功能自上至下, 功能自上至上, 功能自上至下, 功能自上至下, 功能自上至下, 功能自上至下, 功能自上至下。 功能自下至下至上至下, 功能自上至上至下, 功能自上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至上至再再再再再再再再再再再再再再再再再再再可再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再再