Multi-scale object detection in high-resolution remote sensing images faces multiple technical challenges, including missed detection of densely arranged targets, interference between features with scale variations, and insufficient recognition accuracy for small targets. This study proposes a progressive network optimization framework incorporating deformable convolutional networks to enhance deformation-aware feature representation. Furthermore, we construct a YOLOv7-bw single-stage detector that implements a dynamic gradient gain-weighted Intersection over Union (IoU) loss function, which effectively balances the optimization weights for anchor boxes of varying quality. Results show the proposed method outperforms mainstream detectors in mAP and F1-score, while achieving an optimal balance between model complexity and detection performance. This technical solution provides robust support for the intelligent interpretation of remote sensing imagery.