Aiming at the problem that the fusion method used by the existing multi-scale object detection model in the face of scale variation and occlusion scene is not sufficient， and does not capture the long-distance dependency relationship， channel feature fusion aggregation module and non-local feature interaction module are designed to learn the correlation between different channel features and capture the long-distance dependence between feature maps. In addition， the current detection architecture is based on single pyramid detection structure， which exists information loss. In this paper， a double pyramid structure is designed， and the proposed fusion method is combined with the double feature pyramid structure to supplement the fusion feature information on the basis of preserving the original feature information. Experimental results on public datasets KITTI and PASCAL VOC show that the proposed method has higher detection accuracy than other advanced work， proving its effectiveness in object detection task.