Detecting small unauthorized UAVs against complex backgrounds is challenging: targets can be just a few pixels wide, background clutter is pervasive, and the detection system must run on resource-constrained edge hardware. This paper presents a lightweight detector built on YOLOv11 that jointly addresses background suppression and edge-deployment efficiency. A serial channel-spatial attention module is embedded into the backbone to sharpen target features and suppress interference. DySample preserves edge details during resolution recovery, and the detection head is restructured by adding a high-resolution P2 branch while removing the large-object P5 head, shifting the focus to small targets without inflating the model. The WIoU v3 loss function stabilizes training by automatically down-weighting low-quality samples. On the DUT Anti-UAV dataset, the proposed model obtains 92.2% mAP@0.5 and 61.5% mAP@0.5:0.95 with only 2.11 M parameters, a reduction of 18.2% relative to the YOLOv11n baseline. Its ability to generalize across different scenarios is verified by cross-dataset evaluation on Det-Fly and LRDDv2. Deployed on an NVIDIA Jetson Orin Nano Super with TensorRT FP16 optimization, the system achieves 37 end-to-end FPS. These findings demonstrate that the presented method significantly improves small-target detection in complex backgrounds while maintaining a lightweight design suitable for real-time edge deployment.