A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion

Huang Jian-hang; Wang Zhen-you

doi:10.12052/gdutxb.200147

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A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion

更新时间：2026-05-27

- A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion
- Vol. 38, Issue 4, Pages: 52-58(2021)
- 作者机构：
  
  广东工业大学应用数学学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200147
  CLC： TP242.6+2
- Online First：10 July 2021，
  
  Published：2021
- 稿件说明：
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黄剑航, 王振友. 基于特征融合的深度学习目标检测算法研究[J]. 广东工业大学学报, 2021, 38(4): 52-58.

Huang Jian-hang, Wang Zhen-you. A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion[J]. 2021, 38(4): 52-58.
黄剑航, 王振友. 基于特征融合的深度学习目标检测算法研究[J]. 广东工业大学学报, 2021, 38(4): 52-58. DOI： 10.12052/gdutxb.200147.

Huang Jian-hang, Wang Zhen-you. A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion[J]. 2021, 38(4): 52-58. DOI： 10.12052/gdutxb.200147.

摘要

通过研究卷积神经网络中的特征层级

发现高层特征图的分辨率低、语义信息强

低层特征图的分辨率强、语义信息较弱等问题。针对上述问题提出一种二次特征融合的目标检测算法

该算法在特征金字塔网络(Feature Pyramid Networks

FPN)的基础上对过渡特征重复使用并进行二次特征融合

使丰富的低层特征信息补充到高层。最终在COCO2014的数据集上平均精度AP(Average Precision)、AP

、AP

分别达到了35.3%

57.5%

36.6%

与未使用特征融合方法以及使用传统特征融合的方法相比

分别提升了2.4%

3.7%

2.4%

能改善漏检情况和有利于小目标的检测。

Abstract

Through the study of feature levels in convolutional neural networks

this paper found that high-level feature have stronger semantic information and low resolution

and low-level features have strong resolution and weaker semantic information. Aiming at these problems

a object detection algorithm based on secondary feature fusion is proposed. The algorithm reuses transitional features and performs secondary feature fusion on the basis of Feature Pyramid Networks to supplement the rich low-level feature information to the top. Finally

the average accuracy of AP

and AP

on the COCO2014 data set reach 35.3%

57.5%

and 36.6%

respectively. Compared with the unused feature fusion method and the traditional feature fusion method

the average accuracy is

increased by 2.4%

3.7% and 2.4%

which significantly improves the missed detection and the detection of small targets.

关键词

Keywords

references

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