To address the challenges of small object size, severe occlusion, and complex backgrounds in UAV aerial images, traditional object detection algorithms often suffer from missed and false detections. To improve detection accuracy, this paper proposes a small object detection algorithm, YOLOv11n-AFD, based on YOLOv11n by integrating attention and feature modulation mechanisms. The method incorporates a Spatial Strip Attention (SSA) module, a Modulation Fusion Module (MFM), and a Manhattan Feature Enhancement (MFE) module to comprehensively enhance the model′s perception and semantic representation of small objects. Within the unchanged YOLOv11n framework, the SSA models horizontal and vertical spatial dependencies to strengthen structural awareness; the MFM refines feature fusion through channel modulation to highlight key information; and the MFE reinforces geometric features while suppressing background interference for deeper enhancement. Experimental results show that YOLOv11n-AFD achieves improvements of 1.8% in precision, 0.8% in recall, and 1.4% in mAP@0.5 over the original YOLOv11n, with mAP@0.5:0.95 increasing to 21.5%, demonstrating superior performance compared with other algorithms.