郑州大学学报（工学版）2024，Vol.45Issue(3)：72-79,8.DOI:10.13705/j.issn.1671-6833.2024.03.001
基于特征融合和混合注意力的小目标检测
Small Object Detection Based on Feature Fusion and Mixed Attention
摘要
Abstract
To address to the low feature information,low detection rates,and high false rate and missing rate in the target detection task,a Tr-SSD algorithm based on multiscale feature fusion and a hybrid attention mechanism was proposed.Firstly,a Resnet50 residual network was utilized as the backbone network for the SSD algorithm to en-hance its feature extraction capabilities.Secondly,a hybrid attention mechanism was designed and applied to the mid-scale feature maps of the network to enhance effective information within the feature maps and establish long-range dependencies between pieces of information.Finally,a FPN(feature pyramid network)structure was formed by using network layers centered around the Transformer instead of the original backbone network in the SSD algo-rithm,which fused feature information of different scales to more accurately locate small targets.Experimental re-sults showed that the Tr-SSD algorithm achieved mAP values of 81.9%,87.5%,and 88.4%on the PASCAL VOC dataset,HRSID dataset,and RSOD remote sensing dataset,respectively.This represented an improvement of 4.7 percentage points,6.8 percentage points,and 9.2 percentage points compared to the original SSD algorithm.Mo-reover,the detection speed could meet the requirements for real-time detection.关键词
小目标检测/注意力机制/特征融合/深度学习/实时检测Key words
small target detection/attention mechanism/feature fusion/deep learning/real-time detection分类
信息技术与安全科学引用本文复制引用
魏明军,王镆涵,刘亚志,李辉..基于特征融合和混合注意力的小目标检测[J].郑州大学学报（工学版）,2024,45(3):72-79,8.基金项目
科技部重点研发项目(2017YFE0135700) （2017YFE0135700）
河北省高等学校科学技术研究项目(ZD2022102) （ZD2022102）
