Dynamic Aerial Small Target Detection Algorithm Based on Compound Zoom Scaling

Unmanned aerial vehicles (UAVs) equipped with computer vision technology have emerged as a po-werful tool for information acquisition and are widely applied across various fields. However, during multi-angle imaging, UAVs often encounter challenges such as a low target pixel ratio and significant background interference, leading to missed detection and false detection. To address these issues, this paper proposes a novel small-object detection algorithm. Firstly, a more efficient backbone network is introduced, and a composite scaling method is employed to optimize the balance among network depth, width, and image resolution. Additionally, an attention mechanism is integ-rated to effectively capture contextual details of targets with varying scales, orientations, and shapes by leveraging the hierarchical connections of the C2f module, and parallel network is further utilized to enhance interactive modeling of small-target positional information. Secondly, to mitigate the issue of low pixel utilization ratio in small-target detection, a DTADH module is designed, and a shared feature interaction module is constructed. This module, coupled with a task alignment predictor, facilitates both target classification and localization allocation, and the task decomposition is performed by dynamically computing task features through an attention mechanism, thereby reducing the number of parameters effectively and enhancing overall performance. Experiments conducted on the VisDrone2019 UAV aerial ima-gery dataset demonstrate that the proposed algorithm improves mAP by 2.1%, reduces FLOPs by 32.5%, and decreases computational complexity, resulting in superior detection performance.