Object detection in remote sensing imagery presents significant challenges due to scale variations and complex backgrounds, especially for small objects. These challenges arise from diverse perspectives and resolutions in aerial and satellite images. We introduce YOLO-PCDM, a novel framework based on YOLOv8, featuring innovations in contextual information extraction, multi-scale aggregation, and multihead mechanisms. Our approach significantly improves the detection of small objects by enhancing sensitivity to varying object sizes and capturing irregular shapes. Key channels are emphasized, leading to superior detection performance. Experiments demonstrate that our method achieves 80.09% mAP on the DIOR dataset and 78.61% mAP on the DOTA dataset, outperforming existing methods. Comprehensive ablation studies confirm the effectiveness of each component, highlighting their synergistic impact. This robust framework holds great promise for applications in environmental monitoring, urban planning, and agricultural management, paving the way for future innovations in remote sensing analysis and real-time applications.