To address the issues of missed and false detections caused by significant scale differences of foreground targets, uneven sample spatial distribution, and high background redundancy in UAV aerial images, an adaptive foreground-focused UAV aerial image target detection algorithm is proposed. A panoramic feature refinement classification layer is constructed to enhance the algorithm's focusing capability and improve the representation quality of foreground sample features through the re-parameterization spatial pixel variance method and shuffling operation. An adaptive dual-dimensional feature sampling unit is designed using a separate-learn-merge strategy to strengthen the algorithm's ability to extract foreground focus features and retain background detail information, thereby improving false detection situations and accelerating inference speed. A multi-path information integration module is constructed by combining a multi-branch structure and a broadcast self-attention mechanism to solve the ambiguity mapping problem caused by downsampling, optimize feature interaction and integration, enhance the algorithm's ability to recognize and locate multi-scale targets, and reduce model computational load. An adaptive foreground-focused detection head is introduced, which employs a dynamic focusing mechanism to enhance foreground target detection accuracy and suppress background interference. Experiments on the public datasets VisDrone2019 and VisDrone2021 show that the proposed method achieves mAP@0.5 values of 45.1% and 43.1%, respectively, improving by 6.6% and 5.7% compared to the baseline model, and outperforming other comparison algorithms. These results demonstrate that the proposed algorithm significantly improves detection accuracy and possesses good generalizability and real-time performance.