计算机与现代化Issue(3)：106-112,7.DOI:10.3969/j.issn.1006-2475.2025.03.016
面向小目标检测的自适应多维度特征融合网络
AMDFF-Net:Adaptive Multi-dimensional Feature Fusion Network for Tiny Object Detection
柳尧凯 1任德均 1刘重宜 1卢宇东1
作者信息
- 1. 四川大学机械工程学院,四川 成都 610065
- 折叠
摘要
Abstract
Tiny object detection is a huge challenge in object detection research because tiny objects take up fewer pixels in the image,which results in a lack of feature information.To address this issue,an adaptive multi-dimensional feature fusion net-work(AMDFF-Net)for tiny target detection is designed to improve the accuracy of tiny object detection.Firstly,by integrating pooling layers and attention mechanisms,this paper constructs a pooling attention module,enabling the model to achieve a larger receptive field to enable self-adaptive and long-range correlations in self-attention.Secondly,an adaptive selection multi-dimensional feature fusion(ASMFF)module is designed,and an adaptive multi-dimensional feature pyramid network is de-signed based on the ASMFF module.This network adaptively fuses image features at different scales to enhance the information about tiny objects.To verify the performance and generalization of the model,experiments are conducted on the VisDrone2019,AI-TOD,and TinyPerson datasets.The experimental results show that AMDFF-Net improves the accuracy of tiny target detec-tion,and the effectiveness of the proposed model in tiny target detection is verified by comparing with other mainstream algo-rithms.关键词
小目标检测/特征金字塔网络/注意力机制/特征融合Key words
tiny object detection/feature pyramid network/attention mechanisms/feature fusion分类
信息技术与安全科学引用本文复制引用
柳尧凯,任德均,刘重宜,卢宇东..面向小目标检测的自适应多维度特征融合网络[J].计算机与现代化,2025,(3):106-112,7.
