基于动态加权注意力的复杂背景小目标检测方法OA

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.