The object detection algorithms based on the feature pyramid network do not give due consideration to the scale differences among different objects and the high-frequency information loss during cross-layer feature fusion, denying the network sufficient fusion of global multi-scale information and consequently resulting in poor detection effects. To solve these problems, this study proposes a scale-enhanced feature pyramid network. This method improves the lateral connection and cross-layer feature fusion modes of the feature pyramid network. Specifically, a multi-scale convolution group with the dynamic receptive field is designed to serve as a lateral connection so that the feature information of each object can be extracted sufficiently, and a high-frequency information enhancement module based on the attention mechanism is introduced to promote the fusion of high-layer features with low-layer ones. The experimental results on the MS COCO dataset show that the proposed method can effectively improve the detection accuracy on objects at each scale and its overall performance is better than that of the existing methods.