HCA-YOLO: A Hierarchical Cross-Scale Attention Learning Framework for UAV Detection

The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as well as the mutable scales. To solve the problems, a hierarchical attention promoted cross-scale learning framework was proposed in this paper. First, the hierarchical attention mechanism was introduced in the backbone to generate the multi-scale features of targets, so they can be discerned and located at different scales. The resulting features were further delivered to the neck, in which two branches of features were built, respectively. The former was obtained by the target-specific feature operator, while the latter was generated by the upsampling operation. The dual branches were further connected in the quasi-residual structure. So the content of targets can be protected well, and the detail information can be reconstructed. Finally, the dynamic focusing loss measurement was presented to regress the bounding box of the target, so the learning effectiveness of presented the architecture can be promoted. To verify the proposed method, multiple rounds of experiments were performed. The results demonstrated that small and weak drones can be detected accurately, especially in adverse lighting and weather conditions. The evaluation metric of mean average precision rate (mAP) can be improved by 18.5% (YOLO6) on the collected dataset.