<p>Target detection is critical for unmanned aerial vehicle (UAV) applications, yet the limited computing and storage resources of UAV platforms pose significant challenges for achieving accurate and efficient detection. This study proposes a lightweight small-object detection algorithm tailored for UAV imagery, integrating lightweight modules to reduce model parameters while maintaining accuracy through streamlined computations and a simplified network architecture. The feature fusion layer is optimized by pruning the branch for large-object detection, thereby eliminating redundant computations, and incorporating an additional feature extraction module to enhance shallow feature representation. Furthermore, a logit-based knowledge distillation strategy is employed, enabling the student model to leverage high-level representations from the teacher model, achieving superior detection performance and improved generalization with fewer parameters and lower computational cost. Compared to the baseline, the proposed method significantly enhances small-object detection accuracy while reducing model complexity.</p>