电子科技大学学报2025，Vol.54Issue(2)：221-232,12.DOI:10.12178/1001-0548.2023270
基于动态自适应通道注意力特征融合的小目标检测
Small object detection based on dynamic adaptive channel attention feature fusion
摘要
Abstract
In order to solve the feature missing and different scales features semantics gaps problem caused by convolution operations in small object detection,a small object detection method based on dynamic adaptive channel attention feature fusion is proposed in this paper.Firstly,a Tri-Neck network structure is introduced to address the semantic gaps and feature deficiency in small object detection across multiple scales.Secondly,a dynamic adaptive channel attention module is proposed to enhance weak semantic features of small objects while suppressing irrelevant information.Additionally,new activation functions and intersection-over-union loss functions are designed within the dynamic adaptive channel attention module to improve channel attention representation capability.Finally,the ResNet50 backbone network is utilized,connecting the feature pyramid network and the Tri-Neck network sequentially.Experimental results on the Pascal VOC 2007 and Pascal VOC 2012 datasets demonstrate performance improvements of 5.3%and 6.2%respectively,while on the MS COCO 2017 dataset,the proposed algorithm shows enhancements in overall performance and small object detection performance by 1.6%and 2%respectively,and on the SODA-D dataset,our proposed algorithm demonstrates superior performance compared to the suboptimal algorithm AP,resulting in a 0.9%improvement in overall accuracy.关键词
小目标检测/多尺度融合特征/特征金字塔/动态通道注意力/交并比损失函数Key words
small object detection/multi-stage feature fusion/feature pyramid network/dynamic channel attention/iIOU loss分类
计算机与自动化引用本文复制引用
吴迪,赵品懿,甘升隆,沈学军,万琴..基于动态自适应通道注意力特征融合的小目标检测[J].电子科技大学学报,2025,54(2):221-232,12.基金项目
国家重点研发计划(2020YFB1713600) （2020YFB1713600）
国家自然科学基金(62476084) （62476084）
湖南省教育厅重点项目(24A0528) （24A0528）
湖南省自然科学基金(2022JJ30198) （2022JJ30198）
湖南省研究生科研创新项目(YC202213) （YC202213）
