SRIN-YOLO: Algorithms for High-Altitude Drone Detection

In response to the problems of easy missed detection, false detection, and limited detection performance in multi-scale and small target scenes of UAVs in complex backgrounds at high altitudes, this paper proposes a lightweight target detection model SRIN-YOLO based on YOLOv11n. In order to enhance the multi-scale feature modeling ability in complex backgrounds, this paper designs a lightweight attention mechanism iREMA that combines inverted residual structure and EMA module to enhance the correlation modeling between features of different scales, thereby improving the feature expression of small targets. In addition, a dynamic weighting strategy is introduced under the Shape-NWD loss function framework, so that the model can adaptively adjust the training weight according to the sample difficulty, so as to pay more attention to the difficult samples and small targets in the training process, and improve the convergence efficiency and detection accuracy of the model. Finally, in terms of network structure, a lightweight backbone and a small target detection head are adopted to achieve a balance between detection accuracy and inference efficiency. The experimental results show that the parameter quantity of SRIN-YOLO is only 1.24 M (2.69MB), which is 51.69% less than that of YOLOv11n. On the TIB-Net dataset, the model achieved 93.2%, 92.5% and 92% on the Precision, Recall and mAP@0.5 indicators, respectively, which were 3%, 13% and 5% higher than the baseline model, respectively, and the inference speed reached 188 FPS. To verify the generalization ability of the model, this paper further conducts experiments on the VisDrone dataset. The results show that SRIN-YOLO is superior to the baseline model in multiple evaluation indicators. The above experimental results show that the proposed method achieves a good balance between detection accuracy and computational efficiency, and is suitable for resource-constrained embedded platforms and real-time drone vision application scenarios.