科技创新与应用2026，Vol.16Issue(16)：168-171,176,5.DOI:10.19981/j.CN23-1581/G3.2026.16.037
基于动态加权注意力的复杂背景小目标检测方法
张欣悦 1廖永为 1陈炜炜1
作者信息
- 1. 深圳城市职业学院,广东 深圳 518038
- 折叠
摘要
Abstract
Small object detection in complex scenes has long been a core challenge due to the low signal-to-noise ratio(SNR)between the target and the background.Background interference often causes small object features to be overshadowed,which usually limits detection performance.To address this,we propose a lightweight dynamic weighting attention mechanism designed as a plug-and-play pre-processing module that can be seamlessly integrated into mainstream object detection frameworks.The dynamic weighting attention mechanism utilizes a learnable 1×1 convolutional kernel as a cross-channel saliency detector to generate dynamic attention weight maps.These weight maps are then used to perform adaptive modulation of the input features,enhancing the target region's feature response while effectively suppressing background interference.Theoretical analysis shows that the mechanism assigns weights based on feature saliency,enhancing the signal-to-noise ratio through the synergistic effect of target enhancement and background suppression,while maintaining sensitivity to the weak features of small objects.From a theoretical perspective,this mechanism provides a novel and practical approach for improving the robustness of small object detection in complex backgrounds.关键词
小目标检测/背景干扰/动态加权注意力机制/特征增强/自适应加权Key words
small object detection/background interference/dynamic weighting attention mechanism/feature enhancement/adaptive weighting分类
信息技术与安全科学引用本文复制引用
张欣悦,廖永为,陈炜炜..基于动态加权注意力的复杂背景小目标检测方法[J].科技创新与应用,2026,16(16):168-171,176,5.
