Abstract: To address the issues of missed and false detections caused by tiny target scales, complex background environments, and extremely uneven target distribution in dense scenes in UAV aerial images, we propose a lightweight small target detection algorithm based on an improved YOLOv11. A light-weight dynamic feature fusion module (LDFM) is designed, which effectively enhances the feature saliency of tiny targets in complex backgrounds through parallel large-kernel depthwise convolutions and geometric feature extraction branches combined with an adaptive gating mechanism. Secondly, a learnable density-adaptive task alignment (LDA-TAL) strategy is proposed, which dynamically adjusts the number of positive samples by estimating grid density, significantly alleviating the problem of insufficient sample allocation in dense occluded scenarios. Finally, a quality-balanced IoU loss (QB-IoU) is introduced, which reweights regression loss according to the matching quality of predicted boxes, improving the localization accuracy of low-quality difficult samples. Experimental results on the VisDrone2019 dataset show that the algorithm achieves an mAP@0.5 of 46.1%, an increase of 11.3% compared with the baseline model, and, while maintaining a lightweight advantage with only 2.87\times 10^6 parameters, the detection accuracy is significantly better than that of existing mainstream detection algorithms.