Small-object detection in urban remote sensing imagery is essential for smart city applications, yet remains challenging due to limited target size, large scale variations, complex urban backgrounds, as well as the trade-off dilemma between detection accuracy and deployment-oriented efficiency. To address these issues, this paper proposes LMGANet, a parameter-efficient and real-time YOLOv11-based detector for urban remote sensing object detection. A C3K2-GDF module is introduced to enhance small-object representation through adaptive receptive-field adjustment and dynamic feature refinement. An Adaptive Multi-scale Feature Aggregation Network (AMFAN) is designed to strengthen cross-scale feature interaction and improve the fusion of spatial details and semantic information. In addition, a Lightweight Enhanced Shared (LES) detection head is developed to reduce parameter redundancy while preserving localization accuracy for small targets. Experiments on the VisDrone2019 and AI-TOD datasets show that LMGANet improves mAP50 by 4.8% and 3.2% over YOLOv11S, respectively, with only 3.63 M parameters and real-time inference capability. These results demonstrate that LMGANet achieves an effective balance among detection accuracy, parameter efficiency, and real-time inference performance for urban remote sensing applications.
Zhu et al. (Thu,) studied this question.