To address challenges in small object detection within drone aerial imagery—such as sparse feature information, intense background interference, and drastic scale variations—this paper proposes YOLO-SR, a lightweight detection algorithm based on attention enhancement and feature reuse mechanisms. First, we designed the lightweight feature extraction module C2f-SA, which incorporates Shuffle Attention. By integrating channel shuffling and grouped spatial attention mechanisms, this module dynamically enhances edge and texture feature responses for small objects, effectively improving the discriminative power of shallow-level features. Second, the Spatial Pyramid Pooling Attention (SPPC) module captures multi-scale contextual information through spatial pyramid pooling. Combined with dual-path (channel and spatial) attention mechanisms, it optimizes feature representation while significantly suppressing complex background interference. Finally, the detection head employs a decoupled architecture separating classification and regression tasks, supplemented by a dynamic loss weighting strategy to mitigate small object localization inaccuracies. Experimental results on the RGBT-Tiny dataset demonstrate that compared to the baseline model YOLOv5s, our algorithm achieves a 5.3% improvement in precision, a 13.1% increase in recall, and respective gains of 11.5% and 22.3% in mAP_0.5 and mAP_0.75, simultaneously reducing the number of parameters by 42.9% (from 7.0 × 10⁶ to 4.0 × 10⁶) and computational cost by 37.2% (from 60.0 GFLOPs to 37.7 GFLOPs). The comprehensive improvement across multiple metrics validates the superiority of the proposed algorithm in both accuracy and efficiency.

Although Transformer-based trackers have achieved impressive tracking accuracy owing to their strong capability for global context modeling, they still suffer from substantial model complexity and high computational latency. To address these limitations, this paper proposes a lightweight Transformer-based single object tracking method, termed TPTTrack. Specifically, a target-state-guided prompt token is introduced and concatenated with the template and search region features. Constructed from compact target-state information, this token guides cross-region feature interaction toward target-relevant information, thereby enhancing tracking robustness. Furthermore, a hierarchical attention decoupling mechanism is developed to improve shallow feature extraction efficiency and reduce redundant self-attention in deeper layers. In addition, a lightweight autoregressive prediction module is employed for dynamic target-state modeling and efficient state estimation. The results of experiments such as 65.0% AO on GOT-10k and 68.5% precision on LaSOT demonstrate an effective balance between accuracy and efficiency that provides a good trade-off between performance and computational cost.