To address the challenges of severe background interference and insufficient multi-scale feature extraction and fusion for small object detection in remote sensing images， which lead to degraded detection accuracy， a Dynamically Perceptive and Cross-Modulated Network （DPCMNet） was proposed. First， to enhance the model’s perception ability to fine-grained objects， the multi-level Hybrid Feature Aggregation （HFA） module was designed to strengthen the response intensity of small object regions dynamically， thereby enriching the textural and structural features effectively. Second， the Dynamic Modulation Fusion （DMF） module was proposed to introduce a joint channel and spatial modulation mechanism， so as to enable adaptive feature interaction， thereby alleviating information loss during multi-scale feature fusion and improving localization and recognition capabilities for small objects. Finally， based on the DMF module， the Dynamic Modulation Fusion Pyramid （DMFP） was constructed to achieve progressive feature fusion and enhancement through cross-scale association between high-level semantic information and low-level detail compensation. Experimental results demonstrate that DPCMNet achieves significant performance improvements on the Unicorn Small Object Dataset （USOD） and VEhicle Detection in Aerial Imagery （VEDAI） remote sensing small object datasets， with Average Precision （AP） increased by 2.9 and 3.1 percentage points， respectively， compared to the baseline model YOLOv11n.