To address the challenges of complex background interference, multi-scale differences in targets, and the difficulty in extracting small targets from remote sensing images, this paper proposes a remote sensing image detection algorithm based on the YOLOv7-tiny model that integrates the visual center mechanism and parallel patch perception. Firstly, the algorithm introduces an explicit visual center mechanism to establish long-distance dependencies between pixels, enriching the overall semantic information of the image and improving the extraction performance of target textures. Secondly, it improves the parallel patch perception module by adjusting the feature extraction receptive fields to adapt to different target scales. Thirdly, a multi-scale feature fusion module is designed to efficiently fuse multi-layer features, thereby improving the model's inference speed. Experimental results on the RSOD dataset show that the proposed algorithm achieves improvements over YOLOv7-tiny in terms of precision, recall, and mean average precision by 1.5%, 2.4%, and 2.4%, respectively. Additionally, validation on the NWPU VHR-10 and DOTA datasets confirms the strong generalization performance of the proposed algorithm. Comparative analysis with other algorithms further demonstrates the superior performance of the proposed approach.