A remote sensing image vehicle detection method combining superpixels and a multi-modal perception network is proposed with the purpose of reducing recognition accuracy due to background interference， target density， and target heterogeneity in remote sensing image vehicle detection. First， based on the region merging rules of hybrid superpixels， the superpixel bipartite graph fusion algorithm was used to fuse the superpixel segmentation results of the two modalities， which improved the accuracy of the superpixel segmentation results of different modal images. Second， MEANet， a vehicle detection method of remote sensing images based on a multi-modal edge aware network， was proposed. An optimized feature pyramid network module was introduced to enhance the ability of the network to learn multi-scale target features. Finally， the two sets of edge features generated by the superpixel and multi-modal fusion module were aggregated through the edge perception module， and the accurate boundary of the vehicle target was generated. Experiments were conducted on the ISPRS Potsdam and ISPRS Vaihingen remote sensing image datasets， and the final scores were 91.05% and 85.11%， respectively. The experimental results showed that the method proposed in this study has good detection accuracy and good application value in high-precision vehicle detection of multi-modal remote sensing images.