Gated Ladder-Shaped Feature Pyramid Network for Object Detection in Optical Remote Sensing Images
Nanqing Liu, Turgay Çelik, Heng-Chao Li
Abstract
This letter presents a new feature pyramid network (FPN) called the gated ladder-shaped FPN (GLFPN) to construct more representative feature pyramids for detecting objects of different sizes in optical remote sensing images. We first use convolution and concatenation operations to fuse three base features extracted by a ResNet backbone. We then obtain multilevel features from these base features. Finally, we use a selective gate to fuse features from multiple levels with equivalent sizes. To evaluate the effectiveness of the proposed GLFPN, we integrate it into the RetinaNet architecture by replacing the conventional FPN. The experimental results on two optical remote sensing image data sets show that the proposed method outperforms the methods compared in this letter.