Aiming at the problem of missed detection and poor detection effect of remote sensing images due to insufficient feature extraction and expression capabilities in complex backgrounds,a YOLOv4 algorithm model that optimizes feature extraction network is proposed.The improved model introduces a new Dense-PANet structure to obtain higher resolution features,and embeds the attention mechanism in the feature extraction network to adapt to remote sensing images due to the large field of view,which leads to the missed detection of small targets in complex backgrounds and the problem of poor detection results.In order to prove the effectiveness of the method proposed in this paper,a comparative experiment was conducted on DIOR remote sensing data sources.The results show that the average accuracy (mean average precision,mAP) of the algorithm in this paper is 86.55%,which is an increase of 2.52% compared to the original algorithm.YOLOv3 and RetinaNet increased by 6.58% and 14.09%,which verifying the effectiveness of the improved algorithm.