To address the challenges of small target sizes, significant scale variations, complex backgrounds and limited computational resources in UAV aerial images, this paper proposes an improved YOLOv11-based algorithm, termed LMH-YOLO. First, we introduce C3k2_PMDRB to enhance multi-scale semantic features generated by cascade expansion paths while reducing the model′s parameter count. Second, we design the LCFI_Net architecture to enable efficient cross-layer information fusion and accurately capture the spatial locations of small targets. Subsequently, a lightweight detection head, LGDHead, is developed to reduce computational overhead and improve efficiency. Finally, the Wv3-MPDIoU loss function is proposed to optimize model convergence and mitigate missed detections. Experimental results demonstrate that on the VisDrone2019 dataset, LMH-YOLO achieves a 4.8% improvement in mAP@50, along with a 42.3% reduction in parameter count and a 32.7% decrease in model size. On the TinyPerson dataset for extremely small targets, mAP@50 increases by 6.7%, while parameter count and model size decrease by 42.3% and 30.8%, respectively. These results indicate that LMH-YOLO attains an optimal balance between performance and model compactness, making it particularly well-suited for small target detection in UAV aerial imagery.