Abstract: With the escalating strategic importance of China's low-altitude economy,precise object detection in aerial imagery under complex scenarios has emerged as a pivotal technology.However,challenges including dense object clustering,intricate background environments,and the prevalence of small targets in aerial images continue to impede feature extraction and detection accuracy for deep learning-based models.This paper introduces an aerial imagery object detection framework integrating dual-domain attention mechanisms and hierarchical feature fusion.Firstly,a channel-spatial dual-domain attention module is engineered to suppress background interference while amplifying salient feature channels through adaptive weight calibration.Secondly,a cross-layer multi-scale feature fusion architecture is developed,incorporating residual fusion pathways and learnable weighting coefficients to enable effective multi-resolution feature interactions.Finally,a dedicated 20×20 pixel small-object detection branch is appended to enhance fine-grained target recognition capabilities.Experimental evaluations on the VisDrone2019 dataset demonstrate substantial performance gains over state-of-the-art baselines:the proposed model achieves relative mAP_0.5 improvements of 97.3%,18.6%,22.7%,33.7%,12.2%,18.4%,and 37.9% compared to Faster R-CNN,LFET-NetYOLOv5x,YOLOv8,YOLOv9,YOLOv10,and YOLOv11 respectively.These results fully demonstrate the effectiveness of the proposed model inaerial image object detection tasks.

Key words: Aerial imagery, Object detection, YOLOv5, Dual-domain attention module, Cross-level multi-scale feature fusion network