Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery

Over the last few years, there has been substantial progress in object detection on remote sensing images (RSIs) where objects are generally distributed with large-scale variations and have different types of orientations. Nevertheless, most of the current convolution neural network approaches lack the ability to deal with the challenges such as size and rotation variations. To address these problems, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed pyramid network extracts features in a wide range of scales and orientations by using novel convolution filters. These features are used to generate vector fields and determine the weight and angle of the highest-scoring orientation for all spatial locations on an image. Finally, the extracted features go through the prediction layers of the detector. The detection performance of the proposed model is validated on two commonly used aerial benchmarks and the results show our propose model can achieve state-of-the-art performance with satisfactory efficiency.