A two-way dense feature pyramid networks for object detection of remote sensing images

The bird's eye view, multi-scale and dense classes in remote sensing images challenge the object detection of remote sensing images. It is not satisfactory to directly apply the object detection method designed for natural scene images to the object detection of remote sensing images. In this paper, we propose a detector with enhanced feature extraction ability to solve the above challenges, namely TWDFPN. TWDFPN has designed the structure of a two-way feature pyramid network (TWFPN) by combining feature maps with different generation directions and different spatial resolutions, which not only improves the utilization of the underlying feature information, but also strengthens the repeated utilization of the feature information of the backbone network, and ultimately improves the feature extraction ability of the network. Meanwhile, the dense-connected module is used in TWFPN to enhance the feature representation ability through limited additional computation cost, which extends the network and deepens the network. To evaluate the effectiveness of the proposed algorithm, this paper carried out experiments on NWPUVHR-10 and RSOD public remote sensing datasets, and the average accuracy (mAP) of 92.98% and 96.16%, respectively, which achieves advanced performance.