This paper proposes a new Few-Shot Object Detection (FSOD) method, which can be trained with only a few samples, allowing the model to recognize new instances. Unlike previous FSOD methods, this work emphasizes the significance of high-and-low resolution features for the detection of object center points in a few samples and proposes a new architecture for few-shot object detection via the combination of high-and-low resolution features. The high-resolution representation module can effectively extract detailed features of the object. Meanwhile, the low-resolution representation module can provide effective semantic information for the object center points, which is more convenient for the network to locate the new object's center and improve the detection accuracy of the network. Several experiments show that our algorithm can reach the most advanced level in the FSOD field, demonstrating its effectiveness.