In remote sensing imagery, detecting small objects is challenging due to the limited representational ability of feature maps when resolution changes. This issue is mainly reflected in two aspects: (1) upsampling causes feature shifts, making feature fusion difficult to align; (2) downsampling leads to the loss of details. Although recent advances in object detection have been remarkable, small-object detection remains unresolved. In this paper, we propose Dual Feature-Aware Sampling YOLO (DFAS-YOLO) to address these issues. First, the Soft-Aware Adaptive Fusion (SAAF) module corrects upsampling by applying adaptive weighting and spatial attention, thereby reducing errors caused by feature shifts. Second, the Global Dense Local Aggregation (GDLA) module employs parallel convolution, max pooling, and average pooling with channel aggregation, combining their strengths to preserve details after downsampling. Furthermore, the detection head is redesigned based on object characteristics in remote sensing imagery. Extensive experiments on the VisDrone2019 and HIT-UAV datasets demonstrate that DFAS-YOLO achieves competitive detection accuracy compared with recent models.