The development of social networks and economic globalization gives logo a great commercial value, which makes logo detection have a good application prospect. In fact, logo objects typically occupy a small portion of the image, and the low resolution of the logo makes it difficult to further improve detection performance. Therefore, this study proposes an improved detection method based on Faster R-CNN. This approach combines the generative adversarial networks and a Faster R-CNN framework, uses the network to map lower resolution features to highly expressed high resolution features, and then sends them to fully connected layers for classification and regression. The outcomes of the experiment are evaluated on a publicly available logo dataset. The results show that the method can effectively improve the accuracy of logo object detection without affecting the detection speed of the basic network.