Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection

Abstract: 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.

• The paper introduces a Parallel Residual Bi-Fusion structure that fuses top-down and bottom-up pathways to improve precise object localization.
• It presents a CORE module for efficient bi-directional feature fusion and a Re-CORE module inspired by ResNet, enhancing semantic feature integration.
• Performance on UAVDT17 and MS COCO shows that PRB-FPN significantly boosts detection accuracy and computational efficiency for varied object sizes.

An Evaluation of the Parallel Residual Bi-Fusion Feature Pyramid Network

The paper "Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection" introduces a novel approach to object detection with the aim to enhance speed and accuracy, particularly in single-shot detection scenarios. This research is grounded in the challenge that existing Feature Pyramid Networks (FPNs) encounter, notably where these FPNs fail to preserve accurate localization, particularly of small objects, due to limitations inherent to pooling operations and an increasing depth in network backbones.

Core Contributions and Design Enhancements

The authors propose the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) to overcome these challenges through various architectural innovations:

1. Parallel Bi-Fusion Structure: The design introduces a parallel bifusion structure which combines feature maps using both top-down and bottom-up pathways. This method aims to retain high-quality features essential for precise localization of objects, irrespective of their size. The parallel approach facilitates the simultaneous detection of small and large objects, thus enhancing the accuracy of detection across varied object scales.
2. Concatenation and Re-Organization (CORE) Module: This module introduces a strategic method for feature fusion, which purifies features in bi-directional fusion pathways, ensuring contextual information is preserved with efficiency. The CORE module functions in a recursive manner, improving feature representation by recovering lost information from lower-layer maps.
3. Residual CORE (Re-CORE) Module: Inspired by ResNet, the addition of this residual design to the CORE module notably contributes to better training ease and integration capability with a wide array of deep learning backbones. This adaptation allows the network to leverage richer semantic features, improving detection rates particularly for small objects.

Evaluation and Unprecedented Performance

The authors benchmark the PRB-FPN against state-of-the-art object detection models on UAVDT17 and MS COCO datasets. The evaluation reveals performance improvements, establishing the proposed architecture as superior in terms of both accuracy and computational efficiency. The inclusion of bottom-up fusion modules further showcases a significant enhancement in the detection of objects across varied sizes.

Implications and Future Research Directions

The implications of this work are significant for practical applications requiring rapid and accurate object detection across varying scales. The parallel residual design facilitates enhanced semantic feature extraction and representation, making it highly applicable in real-time contexts and edge devices where speed and accuracy are paramount.

Potential future research could explore the integration of anchor-free methods with the PRB-FPN to eliminate issues related to pre-defined anchors, which may hinder the adaptability and accuracy of object detection models—particularly in dynamic and diverse environments. Furthermore, automated architecture searches such as NAS could be employed to refine both the backbone and the feature pyramid structure, potentially leading to further improvements.

In conclusion, the PRB-FPN presents a robust advancement in the field of object detection, offering a viable solution to both scale invariance and efficiency, laying the groundwork for future explorations in enhancing machine learning models for real-world applications.