This paper proposes a backbone network for object detection aiming at the difference between object detection and image classification, to solve the problem that most object detectors are excessively dependent on the classification network. The network mainly includes the initial block, feature fusion module, and mix down-sampling module. The initial block can reduce information loss of the input image. By concatenating the outputs of different convolution layers, the feature fusion module not only enhances the robustness of the network to detection objects with various sizes but also provides more context information for object detection, which effectively improves detection accuracy. In the down-sampling part of the network, a mix down-sampling module is introduced, which balances the ability of the backbone network to classify and locate objects. Experimental results show that the mean value of average precision of the proposed model can reach 81.0% on the PASCAL VOC 2007 test set after training on PASCAL VOC 2007 and PASCAL VOC 2012 datasets, and the detection speed of the model is 85 frame/s, which ensures good performance in terms of accuracy and efficiency.